├── .gitignore ├── Chapter 10 └── chapter_10.ipynb ├── Chapter 11 ├── Chapter_11.ipynb ├── Story_generation_with_lstms.ipynb ├── Using__pretrained__embeddings.ipynb └── stories.txt ├── Chapter 12 ├── Retail_store_forecasting_stat_methods.ipynb ├── Time__series__forecasting__with__ML.ipynb └── sales_data.csv ├── Chapter 13 ├── Apple_Stock_forecasting.ipynb ├── Chapter_13_1.ipynb └── Chapter_13_2.ipynb ├── Chapter 2 ├── Chapter_2_Data_Representation.ipynb └── Chapter_2_Hello_World.ipynb ├── Chapter 3 ├── Linear_Regression_with_TensorFlow.ipynb ├── new_hires.csv └── salary_dataset.csv ├── Chapter 4 ├── Classification_with_TensorFlow.ipynb └── Student Drop out dataset.csv ├── Chapter 5 └── Image_classification_with_tensorflow.ipynb ├── Chapter 6 └── Chapter_6.ipynb ├── Chapter 7 ├── Chapter_7_1.ipynb ├── Chapter_7_2.ipynb └── weather dataset.zip ├── Chapter 8 └── Chapter_8.ipynb ├── Chapter 9 └── Chapter_9.ipynb ├── LICENSE └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | desktop.ini 2 | -------------------------------------------------------------------------------- /Chapter 11/Chapter_11.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "43Ksofq7XHA2" 7 | }, 8 | "source": [ 9 | "#CHAPTER 11" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": null, 15 | "metadata": { 16 | "id": "_jrQnUADXKDx" 17 | }, 18 | "outputs": [], 19 | "source": [ 20 | "import pandas as pd\n", 21 | "import tensorflow_datasets as tfds\n", 22 | "import tensorflow as tf\n", 23 | "from tensorflow.keras.preprocessing.text import Tokenizer\n", 24 | "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", 25 | "from tensorflow.keras.utils import to_categorical\n", 26 | "import tensorflow_hub as hub\n", 27 | "from sklearn.model_selection import train_test_split\n", 28 | "from tensorflow.keras.models import Sequential\n", 29 | "from tensorflow.keras.layers import Embedding, SimpleRNN, Conv1D, GlobalMaxPooling1D, LSTM, GRU, Bidirectional, Dense, Flatten\n" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": null, 35 | "metadata": { 36 | "id": "ZWyNYXN6XLeQ" 37 | }, 38 | "outputs": [], 39 | "source": [ 40 | "# Load the dataset\n", 41 | "dataset, info = tfds.load('ag_news_subset', with_info=True, as_supervised=True)\n", 42 | "train_dataset, test_dataset = dataset['train'], dataset['test']" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": null, 48 | "metadata": { 49 | "id": "JSaxSuFlXOCR" 50 | }, 51 | "outputs": [], 52 | "source": [ 53 | "# Tokenize and pad the sequences\n", 54 | "tokenizer = Tokenizer(num_words=20000, oov_token=\"\")\n", 55 | "train_texts = [x[0].numpy().decode('utf-8') for x in train_dataset]\n", 56 | "tokenizer.fit_on_texts(train_texts)\n", 57 | "sequences = tokenizer.texts_to_sequences(train_texts)\n", 58 | "sequences = pad_sequences(sequences, padding='post')" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": null, 64 | "metadata": { 65 | "id": "esj8GGxWXQjp" 66 | }, 67 | "outputs": [], 68 | "source": [ 69 | "# Convert labels to one-hot encoding\n", 70 | "train_labels = [label.numpy() for _, label in train_dataset]\n", 71 | "train_labels = to_categorical(train_labels, num_classes=4) # assuming 4 classes\n", 72 | "\n", 73 | "# Split the training set into training and validation sets\n", 74 | "train_sequences, val_sequences, train_labels, val_labels = train_test_split(sequences, train_labels, test_size=0.2, random_state=42)\n" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": null, 80 | "metadata": { 81 | "id": "f-ToCGv12IRo" 82 | }, 83 | "outputs": [], 84 | "source": [ 85 | "vocab_size=20000\n", 86 | "embedding_dim =64\n", 87 | "max_length=sequences.shape[1]" 88 | ] 89 | }, 90 | { 91 | "cell_type": "code", 92 | "execution_count": null, 93 | "metadata": { 94 | "colab": { 95 | "background_save": true 96 | }, 97 | "id": "UmNOpqHjgdir" 98 | }, 99 | "outputs": [], 100 | "source": [ 101 | "# Define the DNN model\n", 102 | "model_dnn = Sequential([\n", 103 | " Embedding(vocab_size, embedding_dim, input_length=max_length),\n", 104 | " Flatten(),\n", 105 | " tf.keras.layers.Dense(64, activation='relu'),\n", 106 | " Dense(4, activation='softmax')\n", 107 | "])\n", 108 | "\n", 109 | "# Define the CNN model\n", 110 | "model_cnn = Sequential([\n", 111 | " Embedding(vocab_size, embedding_dim, input_length=max_length),\n", 112 | " Conv1D(128, 5, activation='relu'),\n", 113 | " GlobalMaxPooling1D(),\n", 114 | " tf.keras.layers.Dense(64, activation='relu'),\n", 115 | " Dense(4, activation='softmax')\n", 116 | "])\n", 117 | "\n", 118 | "# Define the LSTM model\n", 119 | "model_lstm = Sequential([\n", 120 | " Embedding(vocab_size, embedding_dim, input_length=max_length),\n", 121 | " LSTM(32, return_sequences=True),\n", 122 | " LSTM(32),\n", 123 | " tf.keras.layers.Dense(64, activation='relu'),\n", 124 | " Dense(4, activation='softmax')\n", 125 | "])\n", 126 | "\n", 127 | "\n", 128 | "# Define the BiLSTM model\n", 129 | "model_BiLSTM = Sequential([\n", 130 | " Embedding(vocab_size, embedding_dim, input_length=max_length),\n", 131 | " Bidirectional(LSTM(32, return_sequences=True)),\n", 132 | " Bidirectional(LSTM(16)),\n", 133 | " tf.keras.layers.Dense(64, activation='relu'), # new layer\n", 134 | " Dense(4, activation='softmax')\n", 135 | "])\n", 136 | "\n", 137 | "\n", 138 | "models = [model_cnn, model_dnn, model_lstm, model_BiLSTM]\n", 139 | "\n", 140 | "for model in models:\n", 141 | " model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", 142 | " model.fit(train_sequences, train_labels, epochs=10, validation_data=(val_sequences, val_labels),verbose=False)\n", 143 | "\n" 144 | ] 145 | }, 146 | { 147 | "cell_type": "code", 148 | "execution_count": null, 149 | "metadata": { 150 | "colab": { 151 | "background_save": true 152 | }, 153 | "id": "FUvf-CxIoqds", 154 | "outputId": "0df5c17c-7da6-4481-a30f-5040df4a0dbf" 155 | }, 156 | "outputs": [ 157 | { 158 | "name": "stdout", 159 | "output_type": "stream", 160 | "text": [ 161 | "238/238 [==============================] - 1s 4ms/step - loss: 0.7756 - accuracy: 0.8989\n", 162 | "Model Evaluation - Model_CNN\n", 163 | "Loss: 0.7755934000015259\n", 164 | "Accuracy: 0.8989473581314087\n", 165 | "\n", 166 | "238/238 [==============================] - 1s 2ms/step - loss: 0.7091 - accuracy: 0.8896\n", 167 | "Model Evaluation - Model_DNN\n", 168 | "Loss: 0.7091193199157715\n", 169 | "Accuracy: 0.8896052837371826\n", 170 | "\n", 171 | "238/238 [==============================] - 2s 7ms/step - loss: 0.3211 - accuracy: 0.9008\n", 172 | "Model Evaluation - Model_LSTM\n", 173 | "Loss: 0.32113003730773926\n", 174 | "Accuracy: 0.9007894992828369\n", 175 | "\n", 176 | "238/238 [==============================] - 4s 10ms/step - loss: 0.5618 - accuracy: 0.8916\n", 177 | "Model Evaluation - Model_BiLSTM\n", 178 | "Loss: 0.5618014335632324\n", 179 | "Accuracy: 0.8915789723396301\n", 180 | "\n" 181 | ] 182 | } 183 | ], 184 | "source": [ 185 | "# Evaluate the model\n", 186 | "test_texts = [x[0].numpy().decode('utf-8') for x in test_dataset]\n", 187 | "test_sequences = tokenizer.texts_to_sequences(test_texts)\n", 188 | "test_sequences = pad_sequences(test_sequences, padding='post', maxlen=sequences.shape[1])\n", 189 | "\n", 190 | "test_labels = [label.numpy() for _, label in test_dataset]\n", 191 | "test_labels = to_categorical(test_labels, num_classes=4)\n", 192 | "\n", 193 | "model_names = [\"Model_CNN\", \"Model_DNN\", \"Model_LSTM\", \"Model_BiLSTM\"]\n", 194 | "\n", 195 | "for i, model in enumerate(models):\n", 196 | " loss, accuracy = model.evaluate(test_sequences, test_labels)\n", 197 | "\n", 198 | " print(\"Model Evaluation -\", model_names[i])\n", 199 | " print(\"Loss:\", loss)\n", 200 | " print(\"Accuracy:\", accuracy)\n", 201 | " print()" 202 | ] 203 | } 204 | ], 205 | "metadata": { 206 | "accelerator": "GPU", 207 | "colab": { 208 | "provenance": [] 209 | }, 210 | "kernelspec": { 211 | "display_name": "Python 3", 212 | "name": "python3" 213 | }, 214 | "language_info": { 215 | "name": "python" 216 | } 217 | }, 218 | "nbformat": 4, 219 | "nbformat_minor": 0 220 | } -------------------------------------------------------------------------------- /Chapter 11/Story_generation_with_lstms.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "gpuType": "T4" 8 | }, 9 | "kernelspec": { 10 | "name": "python3", 11 | "display_name": "Python 3" 12 | }, 13 | "language_info": { 14 | "name": "python" 15 | }, 16 | "accelerator": "GPU" 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "code", 21 | "source": [ 22 | "# Importing necessary libraries and modules\n", 23 | "import tensorflow as tf\n", 24 | "from tensorflow.keras.preprocessing.text import Tokenizer\n", 25 | "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", 26 | "from tensorflow.keras.models import Sequential\n", 27 | "from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional\n", 28 | "import numpy as np" 29 | ], 30 | "metadata": { 31 | "id": "I01px1gpyF7p" 32 | }, 33 | "execution_count": null, 34 | "outputs": [] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "source": [ 39 | "# Loading and preprocessing the data\n", 40 | "text = open('stories.txt').read().lower()\n", 41 | "# Initialize a tokenizer\n", 42 | "tokenizer = Tokenizer()\n", 43 | "# Fit the tokenizer on text\n", 44 | "tokenizer.fit_on_texts([text])\n", 45 | "# Calculate the total number of unique words in the text\n", 46 | "total_words = len(tokenizer.word_index) + 1" 47 | ], 48 | "metadata": { 49 | "id": "zMfkaKaYydo6" 50 | }, 51 | "execution_count": null, 52 | "outputs": [] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "source": [ 57 | "# Create input sequences using list of tokens\n", 58 | "input_sequences = []\n", 59 | "for line in text.split('\\n'):\n", 60 | " token_list = tokenizer.texts_to_sequences([line])[0]\n", 61 | " for i in range(1, len(token_list)):\n", 62 | " n_gram_sequence = token_list[:i+1]\n", 63 | " input_sequences.append(n_gram_sequence)" 64 | ], 65 | "metadata": { 66 | "id": "kABWbbZUyfZi" 67 | }, 68 | "execution_count": null, 69 | "outputs": [] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "source": [ 74 | "# Pad sequences for consistent length\n", 75 | "max_sequence_len = max([len(x) for x in input_sequences])\n", 76 | "input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))" 77 | ], 78 | "metadata": { 79 | "id": "GCP1tSqiyf-a" 80 | }, 81 | "execution_count": null, 82 | "outputs": [] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "source": [ 87 | "# Creating predictors and label\n", 88 | "predictors, label = input_sequences[:,:-1], input_sequences[:,-1]\n", 89 | "# Convert label into one-hot encoding\n", 90 | "label = tf.keras.utils.to_categorical(label, num_classes=total_words)\n" 91 | ], 92 | "metadata": { 93 | "id": "dI6CwhOiyjqM" 94 | }, 95 | "execution_count": null, 96 | "outputs": [] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "source": [ 101 | "\n", 102 | "# Building the LSTM model\n", 103 | "model = Sequential([\n", 104 | " Embedding(total_words, 200, input_length=max_sequence_len-1),\n", 105 | " Bidirectional(LSTM(200)),\n", 106 | " Dense(total_words, activation='softmax')\n", 107 | "])\n", 108 | "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", 109 | "\n", 110 | "# Train the model\n", 111 | "history = model.fit(predictors, label, epochs=300, verbose=0) # Train the model for 100 epochs" 112 | ], 113 | "metadata": { 114 | "id": "uW-BaA33ylw6" 115 | }, 116 | "execution_count": null, 117 | "outputs": [] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "source": [ 122 | "# Function to generate a story based on a given seed text\n", 123 | "def story_generator(seed_text, next_words, model, max_sequence_len):\n", 124 | " for _ in range(next_words):\n", 125 | " token_list = tokenizer.texts_to_sequences([seed_text])[0]\n", 126 | " token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')\n", 127 | "\n", 128 | " predictions = model.predict(token_list)\n", 129 | "\n", 130 | " predicted = np.argmax(predictions)\n", 131 | "\n", 132 | " output_word = \"\"\n", 133 | " for word, index in tokenizer.word_index.items():\n", 134 | " if index == predicted:\n", 135 | " output_word = word\n", 136 | " break\n", 137 | "\n", 138 | " seed_text += \" \" + output_word\n", 139 | "\n", 140 | " return seed_text\n" 141 | ], 142 | "metadata": { 143 | "id": "iIDt1RkWynXT" 144 | }, 145 | "execution_count": null, 146 | "outputs": [] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": null, 151 | "metadata": { 152 | "colab": { 153 | "base_uri": "https://localhost:8080/" 154 | }, 155 | "id": "W9N1IyhwyBx7", 156 | "outputId": "30e29a51-8fbf-4cd9-bd09-1584dd81ff04" 157 | }, 158 | "outputs": [ 159 | { 160 | "output_type": "stream", 161 | "name": "stdout", 162 | "text": [ 163 | "1/1 [==============================] - 1s 670ms/step\n", 164 | "1/1 [==============================] - 0s 20ms/step\n", 165 | "1/1 [==============================] - 0s 19ms/step\n", 166 | "1/1 [==============================] - 0s 27ms/step\n", 167 | "1/1 [==============================] - 0s 19ms/step\n", 168 | "1/1 [==============================] - 0s 28ms/step\n", 169 | "1/1 [==============================] - 0s 19ms/step\n", 170 | "1/1 [==============================] - 0s 21ms/step\n", 171 | "1/1 [==============================] - 0s 21ms/step\n", 172 | "1/1 [==============================] - 0s 19ms/step\n", 173 | "1/1 [==============================] - 0s 24ms/step\n", 174 | "1/1 [==============================] - 0s 19ms/step\n", 175 | "1/1 [==============================] - 0s 19ms/step\n", 176 | "1/1 [==============================] - 0s 18ms/step\n", 177 | "1/1 [==============================] - 0s 31ms/step\n", 178 | "1/1 [==============================] - 0s 18ms/step\n", 179 | "1/1 [==============================] - 0s 23ms/step\n", 180 | "1/1 [==============================] - 0s 21ms/step\n", 181 | "1/1 [==============================] - 0s 21ms/step\n", 182 | "1/1 [==============================] - 0s 18ms/step\n", 183 | "1/1 [==============================] - 0s 20ms/step\n", 184 | "1/1 [==============================] - 0s 23ms/step\n", 185 | "1/1 [==============================] - 0s 21ms/step\n", 186 | "1/1 [==============================] - 0s 21ms/step\n", 187 | "1/1 [==============================] - 0s 20ms/step\n", 188 | "1/1 [==============================] - 0s 21ms/step\n", 189 | "1/1 [==============================] - 0s 19ms/step\n", 190 | "1/1 [==============================] - 0s 19ms/step\n", 191 | "1/1 [==============================] - 0s 21ms/step\n", 192 | "1/1 [==============================] - 0s 19ms/step\n", 193 | "1/1 [==============================] - 0s 19ms/step\n", 194 | "1/1 [==============================] - 0s 20ms/step\n", 195 | "1/1 [==============================] - 0s 22ms/step\n", 196 | "1/1 [==============================] - 0s 22ms/step\n", 197 | "1/1 [==============================] - 0s 20ms/step\n", 198 | "1/1 [==============================] - 0s 28ms/step\n", 199 | "1/1 [==============================] - 0s 24ms/step\n", 200 | "1/1 [==============================] - 0s 21ms/step\n", 201 | "1/1 [==============================] - 0s 23ms/step\n", 202 | "1/1 [==============================] - 0s 30ms/step\n", 203 | "1/1 [==============================] - 0s 34ms/step\n", 204 | "1/1 [==============================] - 0s 33ms/step\n", 205 | "1/1 [==============================] - 0s 30ms/step\n", 206 | "1/1 [==============================] - 0s 38ms/step\n", 207 | "1/1 [==============================] - 0s 30ms/step\n", 208 | "1/1 [==============================] - 0s 33ms/step\n", 209 | "1/1 [==============================] - 0s 34ms/step\n", 210 | "1/1 [==============================] - 0s 39ms/step\n", 211 | "1/1 [==============================] - 0s 35ms/step\n", 212 | "1/1 [==============================] - 0s 33ms/step\n", 213 | "In the hustle and bustle of ipoti the city the friends also learned about the wider context of the ancient world including the people who had lived and worshipped in the area they explored nearby archaeological sites and museums uncovering artifacts and stories that shed light on the lives and beliefs of those who had come before\n" 214 | ] 215 | } 216 | ], 217 | "source": [ 218 | "# Generate a story\n", 219 | "input_text= \"In the hustle and bustle of ipoti\"\n", 220 | "print(story_generator(input_text, 50, model, max_sequence_len)) # Print the generated story\n" 221 | ] 222 | } 223 | ] 224 | } -------------------------------------------------------------------------------- /Chapter 12/sales_data.csv: -------------------------------------------------------------------------------- 1 | Date,Sales 2 | 2013-01-01,48.4 3 | 2013-01-02,49.63 4 | 2013-01-03,44.66 5 | 2013-01-04,56.7 6 | 2013-01-05,62.87 7 | 2013-01-06,51.03 8 | 2013-01-07,51.81 9 | 2013-01-08,63.24 10 | 2013-01-09,67.87 11 | 2013-01-10,58.2 12 | 2013-01-11,50.56 13 | 2013-01-12,50.42 14 | 2013-01-13,45.59 15 | 2013-01-14,44.28 16 | 2013-01-15,54.52 17 | 2013-01-16,58.77 18 | 2013-01-17,54.59 19 | 2013-01-18,57.58 20 | 2013-01-19,58.04 21 | 2013-01-20,53.8 22 | 2013-01-21,49.38 23 | 2013-01-22,55.48 24 | 2013-01-23,57.46 25 | 2013-01-24,62.87 26 | 2013-01-25,60.21 27 | 2013-01-26,60.62 28 | 2013-01-27,60.86 29 | 2013-01-28,59.22 30 | 2013-01-29,57.92 31 | 2013-01-30,56.86 32 | 2013-01-31,46.73 33 | 2013-02-01,51.13 34 | 2013-02-02,52.15 35 | 2013-02-03,52.22 36 | 2013-02-04,66.03 37 | 2013-02-05,72.4 38 | 2013-02-06,59.07 39 | 2013-02-07,53.23 40 | 2013-02-08,62.96 41 | 2013-02-09,76.49 42 | 2013-02-10,74.96 43 | 2013-02-11,69.22 44 | 2013-02-12,64.01 45 | 2013-02-13,54.54 46 | 2013-02-14,55.92 47 | 2013-02-15,67.88 48 | 2013-02-16,68.32 49 | 2013-02-17,57.96 50 | 2013-02-18,64.04 51 | 2013-02-19,57.59 52 | 2013-02-20,57.39 53 | 2013-02-21,68.16 54 | 2013-02-22,67.17 55 | 2013-02-23,59.89 56 | 2013-02-24,64.09 57 | 2013-02-25,76.78 58 | 2013-02-26,68.86 59 | 2013-02-27,65.92 60 | 2013-02-28,63.95 61 | 2013-03-01,57.16 62 | 2013-03-02,54.86 63 | 2013-03-03,58.24 64 | 2013-03-04,69.83 65 | 2013-03-05,69.61 66 | 2013-03-06,56.16 67 | 2013-03-07,60.86 68 | 2013-03-08,78.4 69 | 2013-03-09,84.36 70 | 2013-03-10,78.22 71 | 2013-03-11,73.96 72 | 2013-03-12,75.87 73 | 2013-03-13,75.09 74 | 2013-03-14,67.97 75 | 2013-03-15,63.27 76 | 2013-03-16,71.02 77 | 2013-03-17,71.65 78 | 2013-03-18,59.85 79 | 2013-03-19,62.68 80 | 2013-03-20,67.79 81 | 2013-03-21,67.77 82 | 2013-03-22,68.19 83 | 2013-03-23,75.39 84 | 2013-03-24,76.84 85 | 2013-03-25,64.84 86 | 2013-03-26,62.4 87 | 2013-03-27,65.04 88 | 2013-03-28,60.97 89 | 2013-03-29,67.57 90 | 2013-03-30,75.36 91 | 2013-03-31,72.99 92 | 2013-04-01,71.86 93 | 2013-04-02,72.77 94 | 2013-04-03,69.43 95 | 2013-04-04,66.08 96 | 2013-04-05,75.16 97 | 2013-04-06,77.79 98 | 2013-04-07,61.1 99 | 2013-04-08,63.18 100 | 2013-04-09,68.22 101 | 2013-04-10,69.22 102 | 2013-04-11,72.95 103 | 2013-04-12,73.02 104 | 2013-04-13,72.18 105 | 2013-04-14,84.76 106 | 2013-04-15,79.44 107 | 2013-04-16,70.17 108 | 2013-04-17,75.28 109 | 2013-04-18,75.1 110 | 2013-04-19,71.97 111 | 2013-04-20,64.23 112 | 2013-04-21,82.33 113 | 2013-04-22,81.64 114 | 2013-04-23,67.38 115 | 2013-04-24,67.79 116 | 2013-04-25,69.24 117 | 2013-04-26,73.24 118 | 2013-04-27,77.17 119 | 2013-04-28,72.85 120 | 2013-04-29,68.9 121 | 2013-04-30,70.9 122 | 2013-05-01,67.8 123 | 2013-05-02,64.54 124 | 2013-05-03,71.34 125 | 2013-05-04,70.88 126 | 2013-05-05,67.95 127 | 2013-05-06,71.25 128 | 2013-05-07,81.95 129 | 2013-05-08,87.53 130 | 2013-05-09,76.99 131 | 2013-05-10,58.81 132 | 2013-05-11,65.8 133 | 2013-05-12,62.48 134 | 2013-05-13,69.61 135 | 2013-05-14,77.76 136 | 2013-05-15,71.43 137 | 2013-05-16,60.93 138 | 2013-05-17,66.07 139 | 2013-05-18,68.79 140 | 2013-05-19,65.06 141 | 2013-05-20,61.01 142 | 2013-05-21,61.63 143 | 2013-05-22,60.24 144 | 2013-05-23,70.35 145 | 2013-05-24,81.43 146 | 2013-05-25,70.9 147 | 2013-05-26,60.07 148 | 2013-05-27,66.48 149 | 2013-05-28,67.52 150 | 2013-05-29,69.93 151 | 2013-05-30,62.78 152 | 2013-05-31,57.5 153 | 2013-06-01,50.25 154 | 2013-06-02,46.54 155 | 2013-06-03,54.69 156 | 2013-06-04,64.52 157 | 2013-06-05,66.03 158 | 2013-06-06,56.08 159 | 2013-06-07,53.61 160 | 2013-06-08,49.35 161 | 2013-06-09,49.36 162 | 2013-06-10,53.8 163 | 2013-06-11,58.83 164 | 2013-06-12,58.48 165 | 2013-06-13,56.48 166 | 2013-06-14,50.23 167 | 2013-06-15,51.48 168 | 2013-06-16,53.84 169 | 2013-06-17,59.42 170 | 2013-06-18,61.84 171 | 2013-06-19,58.74 172 | 2013-06-20,60.47 173 | 2013-06-21,58.11 174 | 2013-06-22,54.78 175 | 2013-06-23,48.47 176 | 2013-06-24,46.86 177 | 2013-06-25,51.68 178 | 2013-06-26,53.45 179 | 2013-06-27,53.92 180 | 2013-06-28,50.27 181 | 2013-06-29,52.07 182 | 2013-06-30,49.93 183 | 2013-07-01,54.61 184 | 2013-07-02,60.88 185 | 2013-07-03,49.24 186 | 2013-07-04,49.28 187 | 2013-07-05,47.18 188 | 2013-07-06,48.75 189 | 2013-07-07,49.89 190 | 2013-07-08,51.88 191 | 2013-07-09,45.16 192 | 2013-07-10,45.34 193 | 2013-07-11,47.48 194 | 2013-07-12,44.09 195 | 2013-07-13,42.66 196 | 2013-07-14,46.87 197 | 2013-07-15,52.26 198 | 2013-07-16,47.81 199 | 2013-07-17,42.29 200 | 2013-07-18,52.05 201 | 2013-07-19,61.02 202 | 2013-07-20,59.07 203 | 2013-07-21,57.29 204 | 2013-07-22,49.75 205 | 2013-07-23,52.96 206 | 2013-07-24,55.11 207 | 2013-07-25,44.79 208 | 2013-07-26,38.03 209 | 2013-07-27,41.27 210 | 2013-07-28,45.68 211 | 2013-07-29,44.22 212 | 2013-07-30,52.6 213 | 2013-07-31,55.34 214 | 2013-08-01,44.04 215 | 2013-08-02,43.77 216 | 2013-08-03,43.78 217 | 2013-08-04,45.86 218 | 2013-08-05,38.44 219 | 2013-08-06,30.2 220 | 2013-08-07,37.08 221 | 2013-08-08,42.66 222 | 2013-08-09,35.49 223 | 2013-08-10,35.79 224 | 2013-08-11,34.67 225 | 2013-08-12,40.56 226 | 2013-08-13,51.9 227 | 2013-08-14,54.28 228 | 2013-08-15,41.41 229 | 2013-08-16,35.62 230 | 2013-08-17,46.11 231 | 2013-08-18,57.57 232 | 2013-08-19,48.09 233 | 2013-08-20,28.58 234 | 2013-08-21,29.8 235 | 2013-08-22,34.33 236 | 2013-08-23,41.52 237 | 2013-08-24,48.06 238 | 2013-08-25,42.14 239 | 2013-08-26,38.77 240 | 2013-08-27,38.34 241 | 2013-08-28,41.99 242 | 2013-08-29,32.79 243 | 2013-08-30,31.88 244 | 2013-08-31,39.41 245 | 2013-09-01,26.04 246 | 2013-09-02,18.8 247 | 2013-09-03,31.07 248 | 2013-09-04,34.66 249 | 2013-09-05,36.23 250 | 2013-09-06,37.04 251 | 2013-09-07,38.11 252 | 2013-09-08,38.66 253 | 2013-09-09,36.04 254 | 2013-09-10,37.73 255 | 2013-09-11,34.41 256 | 2013-09-12,34.76 257 | 2013-09-13,35.16 258 | 2013-09-14,36.48 259 | 2013-09-15,44.4 260 | 2013-09-16,35.09 261 | 2013-09-17,33.96 262 | 2013-09-18,40.17 263 | 2013-09-19,39.84 264 | 2013-09-20,40.41 265 | 2013-09-21,29.96 266 | 2013-09-22,31.96 267 | 2013-09-23,32.17 268 | 2013-09-24,36.46 269 | 2013-09-25,39.25 270 | 2013-09-26,26.18 271 | 2013-09-27,29.21 272 | 2013-09-28,31.0 273 | 2013-09-29,29.2 274 | 2013-09-30,22.4 275 | 2013-10-01,27.42 276 | 2013-10-02,33.13 277 | 2013-10-03,33.93 278 | 2013-10-04,38.3 279 | 2013-10-05,37.76 280 | 2013-10-06,48.07 281 | 2013-10-07,44.0 282 | 2013-10-08,32.38 283 | 2013-10-09,27.14 284 | 2013-10-10,19.89 285 | 2013-10-11,31.14 286 | 2013-10-12,42.07 287 | 2013-10-13,37.08 288 | 2013-10-14,34.36 289 | 2013-10-15,40.31 290 | 2013-10-16,40.38 291 | 2013-10-17,34.43 292 | 2013-10-18,35.25 293 | 2013-10-19,42.55 294 | 2013-10-20,38.05 295 | 2013-10-21,31.83 296 | 2013-10-22,35.77 297 | 2013-10-23,40.15 298 | 2013-10-24,38.44 299 | 2013-10-25,32.29 300 | 2013-10-26,42.27 301 | 2013-10-27,45.05 302 | 2013-10-28,32.42 303 | 2013-10-29,43.58 304 | 2013-10-30,46.64 305 | 2013-10-31,40.19 306 | 2013-11-01,34.88 307 | 2013-11-02,43.14 308 | 2013-11-03,41.81 309 | 2013-11-04,37.81 310 | 2013-11-05,30.93 311 | 2013-11-06,37.45 312 | 2013-11-07,46.96 313 | 2013-11-08,35.17 314 | 2013-11-09,32.7 315 | 2013-11-10,46.42 316 | 2013-11-11,48.56 317 | 2013-11-12,36.93 318 | 2013-11-13,32.9 319 | 2013-11-14,39.08 320 | 2013-11-15,48.43 321 | 2013-11-16,38.3 322 | 2013-11-17,31.7 323 | 2013-11-18,42.71 324 | 2013-11-19,43.66 325 | 2013-11-20,50.23 326 | 2013-11-21,55.4 327 | 2013-11-22,41.87 328 | 2013-11-23,41.89 329 | 2013-11-24,47.73 330 | 2013-11-25,45.64 331 | 2013-11-26,40.49 332 | 2013-11-27,44.26 333 | 2013-11-28,51.07 334 | 2013-11-29,56.57 335 | 2013-11-30,44.8 336 | 2013-12-01,44.54 337 | 2013-12-02,51.88 338 | 2013-12-03,59.72 339 | 2013-12-04,58.29 340 | 2013-12-05,46.27 341 | 2013-12-06,48.93 342 | 2013-12-07,47.94 343 | 2013-12-08,46.08 344 | 2013-12-09,54.98 345 | 2013-12-10,47.69 346 | 2013-12-11,35.54 347 | 2013-12-12,43.68 348 | 2013-12-13,47.4 349 | 2013-12-14,51.38 350 | 2013-12-15,59.93 351 | 2013-12-16,57.75 352 | 2013-12-17,58.6 353 | 2013-12-18,50.5 354 | 2013-12-19,51.6 355 | 2013-12-20,50.16 356 | 2013-12-21,39.62 357 | 2013-12-22,47.54 358 | 2013-12-23,48.25 359 | 2013-12-24,45.42 360 | 2013-12-25,49.28 361 | 2013-12-26,50.55 362 | 2013-12-27,46.43 363 | 2013-12-28,49.71 364 | 2013-12-29,53.4 365 | 2013-12-30,48.65 366 | 2013-12-31,48.86 367 | 2014-01-01,49.9 368 | 2014-01-02,54.89 369 | 2014-01-03,52.08 370 | 2014-01-04,52.92 371 | 2014-01-05,60.48 372 | 2014-01-06,60.44 373 | 2014-01-07,51.23 374 | 2014-01-08,53.15 375 | 2014-01-09,58.13 376 | 2014-01-10,64.36 377 | 2014-01-11,60.03 378 | 2014-01-12,56.4 379 | 2014-01-13,49.58 380 | 2014-01-14,58.73 381 | 2014-01-15,62.85 382 | 2014-01-16,61.03 383 | 2014-01-17,62.08 384 | 2014-01-18,63.21 385 | 2014-01-19,61.28 386 | 2014-01-20,60.31 387 | 2014-01-21,68.26 388 | 2014-01-22,64.0 389 | 2014-01-23,58.71 390 | 2014-01-24,58.25 391 | 2014-01-25,59.95 392 | 2014-01-26,65.06 393 | 2014-01-27,59.34 394 | 2014-01-28,66.1 395 | 2014-01-29,70.76 396 | 2014-01-30,65.0 397 | 2014-01-31,69.91 398 | 2014-02-01,64.88 399 | 2014-02-02,65.01 400 | 2014-02-03,71.36 401 | 2014-02-04,67.46 402 | 2014-02-05,57.29 403 | 2014-02-06,58.54 404 | 2014-02-07,67.9 405 | 2014-02-08,64.52 406 | 2014-02-09,57.71 407 | 2014-02-10,70.53 408 | 2014-02-11,67.23 409 | 2014-02-12,58.89 410 | 2014-02-13,67.07 411 | 2014-02-14,65.92 412 | 2014-02-15,60.36 413 | 2014-02-16,55.1 414 | 2014-02-17,60.88 415 | 2014-02-18,68.22 416 | 2014-02-19,71.68 417 | 2014-02-20,72.8 418 | 2014-02-21,75.82 419 | 2014-02-22,83.02 420 | 2014-02-23,83.97 421 | 2014-02-24,77.47 422 | 2014-02-25,72.09 423 | 2014-02-26,73.46 424 | 2014-02-27,66.85 425 | 2014-02-28,65.36 426 | 2014-03-01,67.14 427 | 2014-03-02,72.13 428 | 2014-03-03,72.11 429 | 2014-03-04,72.52 430 | 2014-03-05,65.74 431 | 2014-03-06,64.12 432 | 2014-03-07,69.22 433 | 2014-03-08,70.92 434 | 2014-03-09,70.49 435 | 2014-03-10,76.59 436 | 2014-03-11,77.58 437 | 2014-03-12,73.42 438 | 2014-03-13,68.9 439 | 2014-03-14,79.92 440 | 2014-03-15,88.79 441 | 2014-03-16,83.01 442 | 2014-03-17,79.51 443 | 2014-03-18,78.31 444 | 2014-03-19,75.38 445 | 2014-03-20,68.77 446 | 2014-03-21,75.52 447 | 2014-03-22,77.31 448 | 2014-03-23,73.92 449 | 2014-03-24,81.59 450 | 2014-03-25,75.36 451 | 2014-03-26,76.25 452 | 2014-03-27,80.16 453 | 2014-03-28,73.29 454 | 2014-03-29,70.37 455 | 2014-03-30,74.77 456 | 2014-03-31,71.17 457 | 2014-04-01,74.57 458 | 2014-04-02,71.57 459 | 2014-04-03,73.49 460 | 2014-04-04,79.86 461 | 2014-04-05,77.12 462 | 2014-04-06,73.69 463 | 2014-04-07,75.98 464 | 2014-04-08,77.47 465 | 2014-04-09,74.15 466 | 2014-04-10,76.54 467 | 2014-04-11,82.82 468 | 2014-04-12,86.4 469 | 2014-04-13,83.47 470 | 2014-04-14,78.54 471 | 2014-04-15,74.73 472 | 2014-04-16,71.78 473 | 2014-04-17,68.32 474 | 2014-04-18,65.42 475 | 2014-04-19,79.84 476 | 2014-04-20,93.02 477 | 2014-04-21,85.16 478 | 2014-04-22,73.8 479 | 2014-04-23,77.32 480 | 2014-04-24,79.26 481 | 2014-04-25,79.35 482 | 2014-04-26,78.56 483 | 2014-04-27,77.12 484 | 2014-04-28,83.64 485 | 2014-04-29,83.63 486 | 2014-04-30,72.12 487 | 2014-05-01,66.0 488 | 2014-05-02,68.76 489 | 2014-05-03,80.09 490 | 2014-05-04,75.11 491 | 2014-05-05,76.75 492 | 2014-05-06,69.26 493 | 2014-05-07,73.77 494 | 2014-05-08,79.21 495 | 2014-05-09,71.51 496 | 2014-05-10,75.56 497 | 2014-05-11,81.29 498 | 2014-05-12,71.12 499 | 2014-05-13,70.94 500 | 2014-05-14,69.1 501 | 2014-05-15,73.94 502 | 2014-05-16,67.36 503 | 2014-05-17,58.8 504 | 2014-05-18,58.34 505 | 2014-05-19,63.52 506 | 2014-05-20,64.08 507 | 2014-05-21,74.02 508 | 2014-05-22,70.32 509 | 2014-05-23,72.64 510 | 2014-05-24,68.6 511 | 2014-05-25,63.37 512 | 2014-05-26,65.35 513 | 2014-05-27,67.19 514 | 2014-05-28,67.32 515 | 2014-05-29,71.4 516 | 2014-05-30,61.49 517 | 2014-05-31,66.84 518 | 2014-06-01,71.44 519 | 2014-06-02,62.37 520 | 2014-06-03,62.05 521 | 2014-06-04,66.99 522 | 2014-06-05,56.01 523 | 2014-06-06,53.36 524 | 2014-06-07,52.47 525 | 2014-06-08,62.7 526 | 2014-06-09,77.46 527 | 2014-06-10,66.01 528 | 2014-06-11,59.7 529 | 2014-06-12,60.07 530 | 2014-06-13,61.24 531 | 2014-06-14,70.24 532 | 2014-06-15,69.11 533 | 2014-06-16,66.57 534 | 2014-06-17,68.43 535 | 2014-06-18,58.24 536 | 2014-06-19,59.41 537 | 2014-06-20,62.74 538 | 2014-06-21,53.84 539 | 2014-06-22,55.55 540 | 2014-06-23,67.83 541 | 2014-06-24,64.89 542 | 2014-06-25,57.27 543 | 2014-06-26,55.28 544 | 2014-06-27,54.73 545 | 2014-06-28,50.09 546 | 2014-06-29,55.27 547 | 2014-06-30,54.88 548 | 2014-07-01,51.09 549 | 2014-07-02,52.39 550 | 2014-07-03,57.95 551 | 2014-07-04,54.85 552 | 2014-07-05,58.09 553 | 2014-07-06,48.69 554 | 2014-07-07,45.73 555 | 2014-07-08,53.5 556 | 2014-07-09,62.85 557 | 2014-07-10,66.23 558 | 2014-07-11,57.52 559 | 2014-07-12,50.45 560 | 2014-07-13,49.6 561 | 2014-07-14,47.78 562 | 2014-07-15,50.0 563 | 2014-07-16,59.33 564 | 2014-07-17,47.01 565 | 2014-07-18,33.02 566 | 2014-07-19,43.6 567 | 2014-07-20,52.14 568 | 2014-07-21,49.59 569 | 2014-07-22,49.81 570 | 2014-07-23,47.05 571 | 2014-07-24,44.64 572 | 2014-07-25,48.33 573 | 2014-07-26,47.47 574 | 2014-07-27,53.46 575 | 2014-07-28,47.73 576 | 2014-07-29,38.81 577 | 2014-07-30,41.41 578 | 2014-07-31,50.88 579 | 2014-08-01,52.36 580 | 2014-08-02,49.99 581 | 2014-08-03,55.08 582 | 2014-08-04,53.13 583 | 2014-08-05,48.62 584 | 2014-08-06,38.03 585 | 2014-08-07,39.8 586 | 2014-08-08,47.92 587 | 2014-08-09,47.32 588 | 2014-08-10,39.43 589 | 2014-08-11,42.78 590 | 2014-08-12,42.22 591 | 2014-08-13,47.16 592 | 2014-08-14,49.86 593 | 2014-08-15,48.27 594 | 2014-08-16,47.27 595 | 2014-08-17,39.9 596 | 2014-08-18,45.99 597 | 2014-08-19,48.57 598 | 2014-08-20,49.01 599 | 2014-08-21,52.74 600 | 2014-08-22,40.0 601 | 2014-08-23,42.55 602 | 2014-08-24,47.02 603 | 2014-08-25,35.96 604 | 2014-08-26,39.14 605 | 2014-08-27,42.96 606 | 2014-08-28,40.49 607 | 2014-08-29,36.68 608 | 2014-08-30,42.11 609 | 2014-08-31,43.28 610 | 2014-09-01,41.79 611 | 2014-09-02,36.83 612 | 2014-09-03,27.53 613 | 2014-09-04,38.15 614 | 2014-09-05,47.06 615 | 2014-09-06,39.96 616 | 2014-09-07,33.77 617 | 2014-09-08,46.69 618 | 2014-09-09,47.24 619 | 2014-09-10,43.0 620 | 2014-09-11,36.27 621 | 2014-09-12,30.21 622 | 2014-09-13,40.26 623 | 2014-09-14,48.3 624 | 2014-09-15,40.93 625 | 2014-09-16,43.16 626 | 2014-09-17,34.55 627 | 2014-09-18,33.26 628 | 2014-09-19,39.97 629 | 2014-09-20,30.02 630 | 2014-09-21,31.42 631 | 2014-09-22,38.09 632 | 2014-09-23,34.09 633 | 2014-09-24,30.14 634 | 2014-09-25,32.96 635 | 2014-09-26,30.99 636 | 2014-09-27,29.81 637 | 2014-09-28,32.67 638 | 2014-09-29,46.56 639 | 2014-09-30,42.35 640 | 2014-10-01,32.56 641 | 2014-10-02,32.99 642 | 2014-10-03,37.13 643 | 2014-10-04,28.82 644 | 2014-10-05,29.02 645 | 2014-10-06,37.79 646 | 2014-10-07,30.57 647 | 2014-10-08,23.3 648 | 2014-10-09,33.36 649 | 2014-10-10,43.34 650 | 2014-10-11,29.52 651 | 2014-10-12,33.56 652 | 2014-10-13,48.89 653 | 2014-10-14,42.86 654 | 2014-10-15,30.33 655 | 2014-10-16,35.95 656 | 2014-10-17,44.1 657 | 2014-10-18,34.36 658 | 2014-10-19,30.66 659 | 2014-10-20,43.6 660 | 2014-10-21,47.14 661 | 2014-10-22,35.97 662 | 2014-10-23,35.11 663 | 2014-10-24,46.83 664 | 2014-10-25,53.39 665 | 2014-10-26,55.17 666 | 2014-10-27,56.99 667 | 2014-10-28,42.01 668 | 2014-10-29,35.34 669 | 2014-10-30,44.46 670 | 2014-10-31,36.38 671 | 2014-11-01,30.83 672 | 2014-11-02,36.58 673 | 2014-11-03,37.4 674 | 2014-11-04,31.11 675 | 2014-11-05,39.81 676 | 2014-11-06,45.46 677 | 2014-11-07,38.1 678 | 2014-11-08,43.99 679 | 2014-11-09,43.18 680 | 2014-11-10,46.55 681 | 2014-11-11,54.5 682 | 2014-11-12,48.85 683 | 2014-11-13,43.18 684 | 2014-11-14,46.17 685 | 2014-11-15,40.68 686 | 2014-11-16,41.67 687 | 2014-11-17,41.87 688 | 2014-11-18,46.11 689 | 2014-11-19,44.6 690 | 2014-11-20,42.64 691 | 2014-11-21,47.35 692 | 2014-11-22,38.92 693 | 2014-11-23,42.24 694 | 2014-11-24,52.64 695 | 2014-11-25,47.05 696 | 2014-11-26,55.85 697 | 2014-11-27,54.25 698 | 2014-11-28,43.81 699 | 2014-11-29,41.67 700 | 2014-11-30,45.9 701 | 2014-12-01,46.74 702 | 2014-12-02,50.79 703 | 2014-12-03,48.69 704 | 2014-12-04,55.56 705 | 2014-12-05,59.72 706 | 2014-12-06,57.89 707 | 2014-12-07,62.48 708 | 2014-12-08,54.59 709 | 2014-12-09,66.48 710 | 2014-12-10,63.27 711 | 2014-12-11,51.48 712 | 2014-12-12,48.83 713 | 2014-12-13,43.8 714 | 2014-12-14,45.94 715 | 2014-12-15,50.11 716 | 2014-12-16,59.85 717 | 2014-12-17,61.95 718 | 2014-12-18,59.28 719 | 2014-12-19,57.88 720 | 2014-12-20,58.72 721 | 2014-12-21,54.58 722 | 2014-12-22,52.17 723 | 2014-12-23,50.31 724 | 2014-12-24,53.52 725 | 2014-12-25,55.13 726 | 2014-12-26,65.33 727 | 2014-12-27,67.34 728 | 2014-12-28,63.27 729 | 2014-12-29,57.13 730 | 2014-12-30,62.38 731 | 2014-12-31,71.13 732 | 2015-01-01,70.61 733 | 2015-01-02,61.31 734 | 2015-01-03,58.76 735 | 2015-01-04,63.92 736 | 2015-01-05,56.22 737 | 2015-01-06,58.14 738 | 2015-01-07,67.63 739 | 2015-01-08,68.65 740 | 2015-01-09,61.3 741 | 2015-01-10,55.12 742 | 2015-01-11,51.89 743 | 2015-01-12,62.99 744 | 2015-01-13,73.56 745 | 2015-01-14,74.53 746 | 2015-01-15,65.54 747 | 2015-01-16,61.45 748 | 2015-01-17,62.97 749 | 2015-01-18,59.5 750 | 2015-01-19,54.42 751 | 2015-01-20,57.6 752 | 2015-01-21,56.55 753 | 2015-01-22,62.8 754 | 2015-01-23,73.96 755 | 2015-01-24,70.44 756 | 2015-01-25,73.03 757 | 2015-01-26,78.26 758 | 2015-01-27,73.14 759 | 2015-01-28,68.99 760 | 2015-01-29,67.12 761 | 2015-01-30,58.73 762 | 2015-01-31,53.96 763 | 2015-02-01,69.42 764 | 2015-02-02,74.57 765 | 2015-02-03,74.47 766 | 2015-02-04,81.45 767 | 2015-02-05,77.79 768 | 2015-02-06,67.93 769 | 2015-02-07,74.66 770 | 2015-02-08,74.69 771 | 2015-02-09,65.96 772 | 2015-02-10,78.38 773 | 2015-02-11,79.25 774 | 2015-02-12,81.11 775 | 2015-02-13,72.28 776 | 2015-02-14,63.59 777 | 2015-02-15,68.69 778 | 2015-02-16,77.89 779 | 2015-02-17,80.08 780 | 2015-02-18,86.67 781 | 2015-02-19,86.73 782 | 2015-02-20,75.48 783 | 2015-02-21,72.25 784 | 2015-02-22,80.99 785 | 2015-02-23,81.72 786 | 2015-02-24,81.11 787 | 2015-02-25,85.5 788 | 2015-02-26,74.76 789 | 2015-02-27,76.27 790 | 2015-02-28,85.23 791 | 2015-03-01,82.62 792 | 2015-03-02,75.18 793 | 2015-03-03,73.69 794 | 2015-03-04,78.2 795 | 2015-03-05,83.81 796 | 2015-03-06,80.26 797 | 2015-03-07,75.46 798 | 2015-03-08,79.55 799 | 2015-03-09,84.1 800 | 2015-03-10,85.72 801 | 2015-03-11,83.3 802 | 2015-03-12,82.45 803 | 2015-03-13,75.14 804 | 2015-03-14,70.56 805 | 2015-03-15,85.17 806 | 2015-03-16,83.0 807 | 2015-03-17,78.91 808 | 2015-03-18,77.54 809 | 2015-03-19,71.15 810 | 2015-03-20,68.29 811 | 2015-03-21,68.72 812 | 2015-03-22,77.77 813 | 2015-03-23,80.75 814 | 2015-03-24,80.2 815 | 2015-03-25,82.42 816 | 2015-03-26,82.15 817 | 2015-03-27,75.91 818 | 2015-03-28,81.69 819 | 2015-03-29,92.77 820 | 2015-03-30,93.98 821 | 2015-03-31,82.39 822 | 2015-04-01,75.37 823 | 2015-04-02,77.26 824 | 2015-04-03,78.36 825 | 2015-04-04,86.87 826 | 2015-04-05,88.97 827 | 2015-04-06,86.76 828 | 2015-04-07,77.93 829 | 2015-04-08,80.87 830 | 2015-04-09,77.27 831 | 2015-04-10,72.07 832 | 2015-04-11,77.21 833 | 2015-04-12,87.4 834 | 2015-04-13,90.3 835 | 2015-04-14,78.23 836 | 2015-04-15,74.46 837 | 2015-04-16,75.84 838 | 2015-04-17,73.67 839 | 2015-04-18,85.44 840 | 2015-04-19,86.69 841 | 2015-04-20,83.86 842 | 2015-04-21,81.22 843 | 2015-04-22,80.37 844 | 2015-04-23,81.12 845 | 2015-04-24,78.63 846 | 2015-04-25,77.3 847 | 2015-04-26,85.19 848 | 2015-04-27,84.89 849 | 2015-04-28,81.59 850 | 2015-04-29,84.05 851 | 2015-04-30,73.45 852 | 2015-05-01,72.03 853 | 2015-05-02,74.65 854 | 2015-05-03,79.26 855 | 2015-05-04,77.11 856 | 2015-05-05,75.23 857 | 2015-05-06,77.71 858 | 2015-05-07,82.38 859 | 2015-05-08,76.91 860 | 2015-05-09,72.11 861 | 2015-05-10,76.68 862 | 2015-05-11,87.12 863 | 2015-05-12,85.87 864 | 2015-05-13,84.68 865 | 2015-05-14,82.73 866 | 2015-05-15,82.27 867 | 2015-05-16,81.53 868 | 2015-05-17,71.87 869 | 2015-05-18,77.57 870 | 2015-05-19,87.73 871 | 2015-05-20,76.74 872 | 2015-05-21,83.98 873 | 2015-05-22,81.1 874 | 2015-05-23,66.15 875 | 2015-05-24,69.74 876 | 2015-05-25,76.23 877 | 2015-05-26,80.5 878 | 2015-05-27,77.99 879 | 2015-05-28,79.32 880 | 2015-05-29,78.9 881 | 2015-05-30,74.84 882 | 2015-05-31,74.56 883 | 2015-06-01,75.45 884 | 2015-06-02,77.12 885 | 2015-06-03,69.91 886 | 2015-06-04,71.94 887 | 2015-06-05,69.9 888 | 2015-06-06,65.11 889 | 2015-06-07,57.61 890 | 2015-06-08,69.75 891 | 2015-06-09,74.75 892 | 2015-06-10,75.8 893 | 2015-06-11,68.16 894 | 2015-06-12,72.77 895 | 2015-06-13,66.89 896 | 2015-06-14,58.91 897 | 2015-06-15,64.98 898 | 2015-06-16,72.77 899 | 2015-06-17,69.07 900 | 2015-06-18,63.5 901 | 2015-06-19,71.27 902 | 2015-06-20,61.05 903 | 2015-06-21,60.82 904 | 2015-06-22,73.32 905 | 2015-06-23,81.12 906 | 2015-06-24,71.31 907 | 2015-06-25,53.44 908 | 2015-06-26,56.44 909 | 2015-06-27,72.35 910 | 2015-06-28,76.78 911 | 2015-06-29,74.98 912 | 2015-06-30,70.9 913 | 2015-07-01,64.04 914 | 2015-07-02,56.58 915 | 2015-07-03,54.89 916 | 2015-07-04,63.83 917 | 2015-07-05,68.25 918 | 2015-07-06,58.03 919 | 2015-07-07,55.68 920 | 2015-07-08,67.91 921 | 2015-07-09,62.76 922 | 2015-07-10,58.65 923 | 2015-07-11,63.53 924 | 2015-07-12,68.83 925 | 2015-07-13,70.16 926 | 2015-07-14,62.73 927 | 2015-07-15,60.2 928 | 2015-07-16,59.89 929 | 2015-07-17,60.26 930 | 2015-07-18,72.28 931 | 2015-07-19,70.79 932 | 2015-07-20,68.8 933 | 2015-07-21,65.09 934 | 2015-07-22,67.73 935 | 2015-07-23,59.7 936 | 2015-07-24,44.92 937 | 2015-07-25,43.72 938 | 2015-07-26,49.84 939 | 2015-07-27,57.67 940 | 2015-07-28,53.58 941 | 2015-07-29,46.12 942 | 2015-07-30,58.43 943 | 2015-07-31,64.79 944 | 2015-08-01,57.0 945 | 2015-08-02,52.9 946 | 2015-08-03,50.16 947 | 2015-08-04,45.13 948 | 2015-08-05,50.18 949 | 2015-08-06,47.86 950 | 2015-08-07,50.9 951 | 2015-08-08,56.45 952 | 2015-08-09,52.5 953 | 2015-08-10,47.41 954 | 2015-08-11,48.84 955 | 2015-08-12,43.15 956 | 2015-08-13,41.26 957 | 2015-08-14,37.71 958 | 2015-08-15,44.28 959 | 2015-08-16,50.65 960 | 2015-08-17,45.39 961 | 2015-08-18,51.49 962 | 2015-08-19,42.61 963 | 2015-08-20,39.37 964 | 2015-08-21,45.32 965 | 2015-08-22,47.06 966 | 2015-08-23,53.05 967 | 2015-08-24,41.99 968 | 2015-08-25,33.32 969 | 2015-08-26,38.48 970 | 2015-08-27,48.22 971 | 2015-08-28,55.33 972 | 2015-08-29,57.37 973 | 2015-08-30,42.08 974 | 2015-08-31,37.99 975 | 2015-09-01,42.44 976 | 2015-09-02,43.42 977 | 2015-09-03,45.77 978 | 2015-09-04,52.2 979 | 2015-09-05,46.24 980 | 2015-09-06,54.65 981 | 2015-09-07,49.35 982 | 2015-09-08,44.77 983 | 2015-09-09,31.64 984 | 2015-09-10,39.62 985 | 2015-09-11,43.41 986 | 2015-09-12,35.22 987 | 2015-09-13,36.39 988 | 2015-09-14,42.51 989 | 2015-09-15,41.6 990 | 2015-09-16,48.74 991 | 2015-09-17,47.36 992 | 2015-09-18,36.6 993 | 2015-09-19,40.41 994 | 2015-09-20,44.49 995 | 2015-09-21,42.09 996 | 2015-09-22,35.59 997 | 2015-09-23,40.17 998 | 2015-09-24,33.81 999 | 2015-09-25,37.63 1000 | 2015-09-26,38.52 1001 | 2015-09-27,46.53 1002 | 2015-09-28,45.96 1003 | 2015-09-29,36.04 1004 | 2015-09-30,43.22 1005 | 2015-10-01,43.78 1006 | 2015-10-02,42.91 1007 | 2015-10-03,50.03 1008 | 2015-10-04,45.25 1009 | 2015-10-05,46.86 1010 | 2015-10-06,43.46 1011 | 2015-10-07,35.97 1012 | 2015-10-08,35.54 1013 | 2015-10-09,43.85 1014 | 2015-10-10,40.36 1015 | 2015-10-11,44.09 1016 | 2015-10-12,48.16 1017 | 2015-10-13,45.11 1018 | 2015-10-14,41.04 1019 | 2015-10-15,33.1 1020 | 2015-10-16,31.94 1021 | 2015-10-17,44.7 1022 | 2015-10-18,44.49 1023 | 2015-10-19,51.65 1024 | 2015-10-20,52.14 1025 | 2015-10-21,44.48 1026 | 2015-10-22,42.0 1027 | 2015-10-23,39.07 1028 | 2015-10-24,42.29 1029 | 2015-10-25,39.8 1030 | 2015-10-26,43.92 1031 | 2015-10-27,51.88 1032 | 2015-10-28,57.12 1033 | 2015-10-29,52.61 1034 | 2015-10-30,48.23 1035 | 2015-10-31,51.2 1036 | 2015-11-01,46.0 1037 | 2015-11-02,49.39 1038 | 2015-11-03,53.47 1039 | 2015-11-04,45.93 1040 | 2015-11-05,48.24 1041 | 2015-11-06,51.72 1042 | 2015-11-07,49.85 1043 | 2015-11-08,51.43 1044 | 2015-11-09,51.55 1045 | 2015-11-10,46.73 1046 | 2015-11-11,46.91 1047 | 2015-11-12,51.95 1048 | 2015-11-13,50.18 1049 | 2015-11-14,61.85 1050 | 2015-11-15,53.62 1051 | 2015-11-16,40.74 1052 | 2015-11-17,46.43 1053 | 2015-11-18,49.28 1054 | 2015-11-19,47.73 1055 | 2015-11-20,46.82 1056 | 2015-11-21,50.21 1057 | 2015-11-22,52.94 1058 | 2015-11-23,49.86 1059 | 2015-11-24,52.23 1060 | 2015-11-25,56.53 1061 | 2015-11-26,51.36 1062 | 2015-11-27,46.51 1063 | 2015-11-28,52.51 1064 | 2015-11-29,51.57 1065 | 2015-11-30,58.29 1066 | 2015-12-01,61.24 1067 | 2015-12-02,54.88 1068 | 2015-12-03,55.98 1069 | 2015-12-04,51.36 1070 | 2015-12-05,54.11 1071 | 2015-12-06,65.55 1072 | 2015-12-07,68.87 1073 | 2015-12-08,61.49 1074 | 2015-12-09,42.73 1075 | 2015-12-10,58.38 1076 | 2015-12-11,64.56 1077 | 2015-12-12,50.01 1078 | 2015-12-13,57.42 1079 | 2015-12-14,57.98 1080 | 2015-12-15,56.46 1081 | 2015-12-16,52.92 1082 | 2015-12-17,65.82 1083 | 2015-12-18,59.79 1084 | 2015-12-19,46.23 1085 | 2015-12-20,58.84 1086 | 2015-12-21,60.59 1087 | 2015-12-22,55.41 1088 | 2015-12-23,56.74 1089 | 2015-12-24,60.32 1090 | 2015-12-25,62.18 1091 | 2015-12-26,58.82 1092 | 2015-12-27,67.7 1093 | 2015-12-28,65.04 1094 | 2015-12-29,62.33 1095 | 2015-12-30,71.67 1096 | 2015-12-31,77.7 1097 | 2016-01-01,69.56 1098 | 2016-01-02,67.72 1099 | 2016-01-03,67.95 1100 | 2016-01-04,60.74 1101 | 2016-01-05,59.27 1102 | 2016-01-06,58.92 1103 | 2016-01-07,68.49 1104 | 2016-01-08,71.67 1105 | 2016-01-09,59.18 1106 | 2016-01-10,64.49 1107 | 2016-01-11,66.44 1108 | 2016-01-12,67.3 1109 | 2016-01-13,65.73 1110 | 2016-01-14,68.23 1111 | 2016-01-15,74.12 1112 | 2016-01-16,68.07 1113 | 2016-01-17,67.37 1114 | 2016-01-18,77.52 1115 | 2016-01-19,76.2 1116 | 2016-01-20,73.12 1117 | 2016-01-21,86.69 1118 | 2016-01-22,82.32 1119 | 2016-01-23,77.65 1120 | 2016-01-24,81.94 1121 | 2016-01-25,72.9 1122 | 2016-01-26,78.9 1123 | 2016-01-27,83.04 1124 | 2016-01-28,82.83 1125 | 2016-01-29,81.79 1126 | 2016-01-30,73.91 1127 | 2016-01-31,70.23 1128 | 2016-02-01,74.73 1129 | 2016-02-02,76.05 1130 | 2016-02-03,85.37 1131 | 2016-02-04,88.67 1132 | 2016-02-05,74.58 1133 | 2016-02-06,64.6 1134 | 2016-02-07,72.1 1135 | 2016-02-08,68.35 1136 | 2016-02-09,66.84 1137 | 2016-02-10,77.64 1138 | 2016-02-11,87.14 1139 | 2016-02-12,91.43 1140 | 2016-02-13,90.47 1141 | 2016-02-14,76.43 1142 | 2016-02-15,75.15 1143 | 2016-02-16,81.21 1144 | 2016-02-17,81.78 1145 | 2016-02-18,86.21 1146 | 2016-02-19,78.1 1147 | 2016-02-20,79.86 1148 | 2016-02-21,82.63 1149 | 2016-02-22,84.69 1150 | 2016-02-23,84.41 1151 | 2016-02-24,85.29 1152 | 2016-02-25,81.5 1153 | 2016-02-26,72.77 1154 | 2016-02-27,73.53 1155 | 2016-02-28,84.32 1156 | 2016-02-29,83.88 1157 | 2016-03-01,80.7 1158 | 2016-03-02,86.4 1159 | 2016-03-03,84.01 1160 | 2016-03-04,74.94 1161 | 2016-03-05,82.63 1162 | 2016-03-06,90.45 1163 | 2016-03-07,84.88 1164 | 2016-03-08,85.09 1165 | 2016-03-09,85.75 1166 | 2016-03-10,74.11 1167 | 2016-03-11,72.77 1168 | 2016-03-12,74.89 1169 | 2016-03-13,83.05 1170 | 2016-03-14,92.02 1171 | 2016-03-15,86.43 1172 | 2016-03-16,89.02 1173 | 2016-03-17,74.49 1174 | 2016-03-18,69.91 1175 | 2016-03-19,80.61 1176 | 2016-03-20,75.74 1177 | 2016-03-21,87.07 1178 | 2016-03-22,89.7 1179 | 2016-03-23,79.56 1180 | 2016-03-24,76.25 1181 | 2016-03-25,87.34 1182 | 2016-03-26,92.65 1183 | 2016-03-27,91.79 1184 | 2016-03-28,91.32 1185 | 2016-03-29,90.74 1186 | 2016-03-30,88.95 1187 | 2016-03-31,77.7 1188 | 2016-04-01,78.63 1189 | 2016-04-02,89.19 1190 | 2016-04-03,90.08 1191 | 2016-04-04,86.88 1192 | 2016-04-05,82.72 1193 | 2016-04-06,77.33 1194 | 2016-04-07,82.88 1195 | 2016-04-08,90.79 1196 | 2016-04-09,99.37 1197 | 2016-04-10,93.92 1198 | 2016-04-11,100.18 1199 | 2016-04-12,90.97 1200 | 2016-04-13,83.5 1201 | 2016-04-14,94.77 1202 | 2016-04-15,101.16 1203 | 2016-04-16,93.69 1204 | 2016-04-17,88.38 1205 | 2016-04-18,80.43 1206 | 2016-04-19,77.41 1207 | 2016-04-20,82.84 1208 | 2016-04-21,84.48 1209 | 2016-04-22,96.56 1210 | 2016-04-23,93.5 1211 | 2016-04-24,84.72 1212 | 2016-04-25,79.64 1213 | 2016-04-26,88.22 1214 | 2016-04-27,92.41 1215 | 2016-04-28,86.54 1216 | 2016-04-29,94.77 1217 | 2016-04-30,93.08 1218 | 2016-05-01,84.41 1219 | 2016-05-02,78.94 1220 | 2016-05-03,84.6 1221 | 2016-05-04,84.77 1222 | 2016-05-05,81.7 1223 | 2016-05-06,78.1 1224 | 2016-05-07,83.17 1225 | 2016-05-08,76.92 1226 | 2016-05-09,77.91 1227 | 2016-05-10,83.46 1228 | 2016-05-11,82.5 1229 | 2016-05-12,74.84 1230 | 2016-05-13,76.51 1231 | 2016-05-14,80.19 1232 | 2016-05-15,83.17 1233 | 2016-05-16,90.01 1234 | 2016-05-17,87.36 1235 | 2016-05-18,93.8 1236 | 2016-05-19,86.66 1237 | 2016-05-20,86.0 1238 | 2016-05-21,82.84 1239 | 2016-05-22,73.28 1240 | 2016-05-23,68.57 1241 | 2016-05-24,66.13 1242 | 2016-05-25,75.44 1243 | 2016-05-26,78.64 1244 | 2016-05-27,77.94 1245 | 2016-05-28,80.92 1246 | 2016-05-29,71.07 1247 | 2016-05-30,65.85 1248 | 2016-05-31,86.73 1249 | 2016-06-01,83.23 1250 | 2016-06-02,69.58 1251 | 2016-06-03,74.25 1252 | 2016-06-04,68.17 1253 | 2016-06-05,69.03 1254 | 2016-06-06,65.43 1255 | 2016-06-07,75.24 1256 | 2016-06-08,70.97 1257 | 2016-06-09,69.48 1258 | 2016-06-10,71.1 1259 | 2016-06-11,69.64 1260 | 2016-06-12,80.47 1261 | 2016-06-13,79.61 1262 | 2016-06-14,77.53 1263 | 2016-06-15,78.56 1264 | 2016-06-16,70.1 1265 | 2016-06-17,65.15 1266 | 2016-06-18,75.11 1267 | 2016-06-19,74.83 1268 | 2016-06-20,74.41 1269 | 2016-06-21,70.6 1270 | 2016-06-22,71.74 1271 | 2016-06-23,68.27 1272 | 2016-06-24,67.68 1273 | 2016-06-25,66.09 1274 | 2016-06-26,68.47 1275 | 2016-06-27,65.25 1276 | 2016-06-28,63.56 1277 | 2016-06-29,64.43 1278 | 2016-06-30,62.95 1279 | 2016-07-01,59.89 1280 | 2016-07-02,58.59 1281 | 2016-07-03,64.6 1282 | 2016-07-04,60.92 1283 | 2016-07-05,65.22 1284 | 2016-07-06,66.79 1285 | 2016-07-07,65.94 1286 | 2016-07-08,65.54 1287 | 2016-07-09,64.03 1288 | 2016-07-10,62.42 1289 | 2016-07-11,63.14 1290 | 2016-07-12,64.08 1291 | 2016-07-13,62.84 1292 | 2016-07-14,59.33 1293 | 2016-07-15,63.62 1294 | 2016-07-16,59.94 1295 | 2016-07-17,66.21 1296 | 2016-07-18,54.24 1297 | 2016-07-19,48.98 1298 | 2016-07-20,55.04 1299 | 2016-07-21,65.36 1300 | 2016-07-22,56.32 1301 | 2016-07-23,53.2 1302 | 2016-07-24,56.15 1303 | 2016-07-25,45.84 1304 | 2016-07-26,45.28 1305 | 2016-07-27,58.07 1306 | 2016-07-28,61.11 1307 | 2016-07-29,60.19 1308 | 2016-07-30,53.07 1309 | 2016-07-31,50.83 1310 | 2016-08-01,59.26 1311 | 2016-08-02,55.73 1312 | 2016-08-03,56.49 1313 | 2016-08-04,66.13 1314 | 2016-08-05,63.61 1315 | 2016-08-06,64.04 1316 | 2016-08-07,62.42 1317 | 2016-08-08,51.54 1318 | 2016-08-09,57.54 1319 | 2016-08-10,59.7 1320 | 2016-08-11,58.79 1321 | 2016-08-12,57.74 1322 | 2016-08-13,52.23 1323 | 2016-08-14,52.08 1324 | 2016-08-15,60.5 1325 | 2016-08-16,46.57 1326 | 2016-08-17,43.95 1327 | 2016-08-18,54.11 1328 | 2016-08-19,56.42 1329 | 2016-08-20,51.0 1330 | 2016-08-21,44.69 1331 | 2016-08-22,40.15 1332 | 2016-08-23,43.13 1333 | 2016-08-24,47.65 1334 | 2016-08-25,45.08 1335 | 2016-08-26,52.62 1336 | 2016-08-27,54.21 1337 | 2016-08-28,53.7 1338 | 2016-08-29,53.98 1339 | 2016-08-30,50.06 1340 | 2016-08-31,56.65 1341 | 2016-09-01,59.42 1342 | 2016-09-02,50.46 1343 | 2016-09-03,45.58 1344 | 2016-09-04,45.97 1345 | 2016-09-05,45.79 1346 | 2016-09-06,51.92 1347 | 2016-09-07,46.96 1348 | 2016-09-08,42.24 1349 | 2016-09-09,51.7 1350 | 2016-09-10,53.84 1351 | 2016-09-11,51.64 1352 | 2016-09-12,51.1 1353 | 2016-09-13,50.36 1354 | 2016-09-14,53.97 1355 | 2016-09-15,56.29 1356 | 2016-09-16,51.97 1357 | 2016-09-17,36.98 1358 | 2016-09-18,39.12 1359 | 2016-09-19,48.71 1360 | 2016-09-20,47.9 1361 | 2016-09-21,47.43 1362 | 2016-09-22,47.02 1363 | 2016-09-23,41.93 1364 | 2016-09-24,49.02 1365 | 2016-09-25,56.47 1366 | 2016-09-26,50.0 1367 | 2016-09-27,40.51 1368 | 2016-09-28,43.96 1369 | 2016-09-29,52.01 1370 | 2016-09-30,46.12 1371 | 2016-10-01,38.69 1372 | 2016-10-02,44.77 1373 | 2016-10-03,45.03 1374 | 2016-10-04,37.35 1375 | 2016-10-05,42.29 1376 | 2016-10-06,42.26 1377 | 2016-10-07,47.57 1378 | 2016-10-08,44.87 1379 | 2016-10-09,43.72 1380 | 2016-10-10,44.15 1381 | 2016-10-11,42.58 1382 | 2016-10-12,39.28 1383 | 2016-10-13,42.81 1384 | 2016-10-14,42.38 1385 | 2016-10-15,44.84 1386 | 2016-10-16,56.81 1387 | 2016-10-17,55.15 1388 | 2016-10-18,36.6 1389 | 2016-10-19,40.74 1390 | 2016-10-20,45.51 1391 | 2016-10-21,50.34 1392 | 2016-10-22,61.42 1393 | 2016-10-23,64.05 1394 | 2016-10-24,50.4 1395 | 2016-10-25,54.7 1396 | 2016-10-26,59.68 1397 | 2016-10-27,49.83 1398 | 2016-10-28,46.23 1399 | 2016-10-29,51.98 1400 | 2016-10-30,56.51 1401 | 2016-10-31,56.63 1402 | 2016-11-01,46.29 1403 | 2016-11-02,46.3 1404 | 2016-11-03,49.17 1405 | 2016-11-04,51.57 1406 | 2016-11-05,51.52 1407 | 2016-11-06,55.29 1408 | 2016-11-07,56.73 1409 | 2016-11-08,50.35 1410 | 2016-11-09,51.68 1411 | 2016-11-10,54.84 1412 | 2016-11-11,53.01 1413 | 2016-11-12,53.8 1414 | 2016-11-13,54.49 1415 | 2016-11-14,60.37 1416 | 2016-11-15,54.3 1417 | 2016-11-16,46.44 1418 | 2016-11-17,52.16 1419 | 2016-11-18,47.97 1420 | 2016-11-19,50.8 1421 | 2016-11-20,58.57 1422 | 2016-11-21,59.98 1423 | 2016-11-22,57.5 1424 | 2016-11-23,56.72 1425 | 2016-11-24,51.66 1426 | 2016-11-25,54.62 1427 | 2016-11-26,63.2 1428 | 2016-11-27,63.1 1429 | 2016-11-28,71.65 1430 | 2016-11-29,79.97 1431 | 2016-11-30,58.89 1432 | 2016-12-01,54.68 1433 | 2016-12-02,50.79 1434 | 2016-12-03,51.39 1435 | 2016-12-04,64.92 1436 | 2016-12-05,66.16 1437 | 2016-12-06,59.08 1438 | 2016-12-07,59.8 1439 | 2016-12-08,68.86 1440 | 2016-12-09,62.16 1441 | 2016-12-10,57.77 1442 | 2016-12-11,66.14 1443 | 2016-12-12,64.18 1444 | 2016-12-13,57.84 1445 | 2016-12-14,64.62 1446 | 2016-12-15,75.27 1447 | 2016-12-16,73.67 1448 | 2016-12-17,67.38 1449 | 2016-12-18,68.49 1450 | 2016-12-19,61.15 1451 | 2016-12-20,59.22 1452 | 2016-12-21,75.98 1453 | 2016-12-22,68.3 1454 | 2016-12-23,63.7 1455 | 2016-12-24,68.91 1456 | 2016-12-25,71.8 1457 | 2016-12-26,71.15 1458 | 2016-12-27,73.19 1459 | 2016-12-28,76.81 1460 | 2016-12-29,70.31 1461 | 2016-12-30,69.24 1462 | 2016-12-31,73.71 1463 | 2017-01-01,74.38 1464 | 2017-01-02,75.65 1465 | 2017-01-03,80.0 1466 | 2017-01-04,72.16 1467 | 2017-01-05,70.14 1468 | 2017-01-06,82.59 1469 | 2017-01-07,81.07 1470 | 2017-01-08,75.1 1471 | 2017-01-09,80.64 1472 | 2017-01-10,83.26 1473 | 2017-01-11,63.92 1474 | 2017-01-12,62.53 1475 | 2017-01-13,62.48 1476 | 2017-01-14,71.19 1477 | 2017-01-15,73.25 1478 | 2017-01-16,70.98 1479 | 2017-01-17,79.16 1480 | 2017-01-18,84.04 1481 | 2017-01-19,78.05 1482 | 2017-01-20,75.18 1483 | 2017-01-21,69.94 1484 | 2017-01-22,67.69 1485 | 2017-01-23,84.63 1486 | 2017-01-24,87.44 1487 | 2017-01-25,84.2 1488 | 2017-01-26,84.07 1489 | 2017-01-27,76.97 1490 | 2017-01-28,77.78 1491 | 2017-01-29,91.19 1492 | 2017-01-30,87.81 1493 | 2017-01-31,82.3 1494 | 2017-02-01,72.23 1495 | 2017-02-02,76.97 1496 | 2017-02-03,84.09 1497 | 2017-02-04,69.52 1498 | 2017-02-05,82.74 1499 | 2017-02-06,89.73 1500 | 2017-02-07,78.7 1501 | 2017-02-08,82.5 1502 | 2017-02-09,86.75 1503 | 2017-02-10,86.26 1504 | 2017-02-11,90.42 1505 | 2017-02-12,80.42 1506 | 2017-02-13,80.13 1507 | 2017-02-14,81.26 1508 | 2017-02-15,75.12 1509 | 2017-02-16,88.44 1510 | 2017-02-17,92.19 1511 | 2017-02-18,76.83 1512 | 2017-02-19,76.31 1513 | 2017-02-20,77.95 1514 | 2017-02-21,81.21 1515 | 2017-02-22,81.94 1516 | 2017-02-23,79.95 1517 | 2017-02-24,81.69 1518 | 2017-02-25,80.69 1519 | 2017-02-26,81.77 1520 | 2017-02-27,84.74 1521 | 2017-02-28,81.42 1522 | 2017-03-01,84.05 1523 | 2017-03-02,82.92 1524 | 2017-03-03,85.24 1525 | 2017-03-04,89.16 1526 | 2017-03-05,103.13 1527 | 2017-03-06,96.14 1528 | 2017-03-07,84.2 1529 | 2017-03-08,90.04 1530 | 2017-03-09,93.78 1531 | 2017-03-10,85.65 1532 | 2017-03-11,91.14 1533 | 2017-03-12,99.64 1534 | 2017-03-13,95.35 1535 | 2017-03-14,88.09 1536 | 2017-03-15,89.37 1537 | 2017-03-16,88.68 1538 | 2017-03-17,83.12 1539 | 2017-03-18,78.94 1540 | 2017-03-19,82.38 1541 | 2017-03-20,86.68 1542 | 2017-03-21,85.94 1543 | 2017-03-22,84.07 1544 | 2017-03-23,93.34 1545 | 2017-03-24,99.44 1546 | 2017-03-25,95.22 1547 | 2017-03-26,90.38 1548 | 2017-03-27,90.89 1549 | 2017-03-28,88.1 1550 | 2017-03-29,83.72 1551 | 2017-03-30,83.22 1552 | 2017-03-31,84.13 1553 | 2017-04-01,86.29 1554 | 2017-04-02,73.27 1555 | 2017-04-03,76.95 1556 | 2017-04-04,82.83 1557 | 2017-04-05,79.18 1558 | 2017-04-06,78.14 1559 | 2017-04-07,74.58 1560 | 2017-04-08,80.24 1561 | 2017-04-09,94.77 1562 | 2017-04-10,93.48 1563 | 2017-04-11,94.97 1564 | 2017-04-12,101.34 1565 | 2017-04-13,100.69 1566 | 2017-04-14,95.66 1567 | 2017-04-15,95.49 1568 | 2017-04-16,88.16 1569 | 2017-04-17,88.84 1570 | 2017-04-18,87.12 1571 | 2017-04-19,87.33 1572 | 2017-04-20,99.47 1573 | 2017-04-21,90.95 1574 | 2017-04-22,90.28 1575 | 2017-04-23,85.29 1576 | 2017-04-24,84.52 1577 | 2017-04-25,90.15 1578 | 2017-04-26,86.83 1579 | 2017-04-27,94.56 1580 | 2017-04-28,92.71 1581 | 2017-04-29,87.78 1582 | 2017-04-30,95.96 1583 | 2017-05-01,101.93 1584 | 2017-05-02,96.11 1585 | 2017-05-03,96.57 1586 | 2017-05-04,94.16 1587 | 2017-05-05,88.8 1588 | 2017-05-06,87.29 1589 | 2017-05-07,82.23 1590 | 2017-05-08,81.76 1591 | 2017-05-09,84.57 1592 | 2017-05-10,77.77 1593 | 2017-05-11,74.77 1594 | 2017-05-12,91.45 1595 | 2017-05-13,98.98 1596 | 2017-05-14,94.48 1597 | 2017-05-15,88.1 1598 | 2017-05-16,76.97 1599 | 2017-05-17,82.21 1600 | 2017-05-18,93.83 1601 | 2017-05-19,97.96 1602 | 2017-05-20,95.53 1603 | 2017-05-21,87.03 1604 | 2017-05-22,79.19 1605 | 2017-05-23,78.73 1606 | 2017-05-24,84.45 1607 | 2017-05-25,86.31 1608 | 2017-05-26,90.47 1609 | 2017-05-27,95.64 1610 | 2017-05-28,95.0 1611 | 2017-05-29,94.43 1612 | 2017-05-30,92.43 1613 | 2017-05-31,86.15 1614 | 2017-06-01,83.95 1615 | 2017-06-02,86.35 1616 | 2017-06-03,76.72 1617 | 2017-06-04,72.1 1618 | 2017-06-05,73.51 1619 | 2017-06-06,77.44 1620 | 2017-06-07,81.71 1621 | 2017-06-08,85.71 1622 | 2017-06-09,87.21 1623 | 2017-06-10,83.97 1624 | 2017-06-11,78.29 1625 | 2017-06-12,86.75 1626 | 2017-06-13,91.2 1627 | 2017-06-14,88.86 1628 | 2017-06-15,82.13 1629 | 2017-06-16,83.54 1630 | 2017-06-17,67.58 1631 | 2017-06-18,73.76 1632 | 2017-06-19,77.3 1633 | 2017-06-20,71.97 1634 | 2017-06-21,76.39 1635 | 2017-06-22,79.25 1636 | 2017-06-23,75.7 1637 | 2017-06-24,77.18 1638 | 2017-06-25,69.69 1639 | 2017-06-26,69.05 1640 | 2017-06-27,78.52 1641 | 2017-06-28,77.29 1642 | 2017-06-29,76.5 1643 | 2017-06-30,70.44 1644 | 2017-07-01,70.91 1645 | 2017-07-02,79.1 1646 | 2017-07-03,80.28 1647 | 2017-07-04,72.7 1648 | 2017-07-05,72.66 1649 | 2017-07-06,63.63 1650 | 2017-07-07,59.06 1651 | 2017-07-08,69.17 1652 | 2017-07-09,75.28 1653 | 2017-07-10,57.67 1654 | 2017-07-11,51.57 1655 | 2017-07-12,57.54 1656 | 2017-07-13,65.77 1657 | 2017-07-14,73.66 1658 | 2017-07-15,70.76 1659 | 2017-07-16,67.06 1660 | 2017-07-17,63.39 1661 | 2017-07-18,61.74 1662 | 2017-07-19,58.87 1663 | 2017-07-20,58.4 1664 | 2017-07-21,66.64 1665 | 2017-07-22,75.56 1666 | 2017-07-23,67.39 1667 | 2017-07-24,64.25 1668 | 2017-07-25,60.0 1669 | 2017-07-26,47.27 1670 | 2017-07-27,57.95 1671 | 2017-07-28,70.32 1672 | 2017-07-29,73.73 1673 | 2017-07-30,73.91 1674 | 2017-07-31,59.65 1675 | 2017-08-01,50.57 1676 | 2017-08-02,59.05 1677 | 2017-08-03,52.65 1678 | 2017-08-04,56.48 1679 | 2017-08-05,69.48 1680 | 2017-08-06,66.73 1681 | 2017-08-07,66.81 1682 | 2017-08-08,61.74 1683 | 2017-08-09,67.39 1684 | 2017-08-10,61.76 1685 | 2017-08-11,58.14 1686 | 2017-08-12,56.03 1687 | 2017-08-13,66.84 1688 | 2017-08-14,69.28 1689 | 2017-08-15,61.59 1690 | 2017-08-16,63.57 1691 | 2017-08-17,67.73 1692 | 2017-08-18,77.69 1693 | 2017-08-19,53.75 1694 | 2017-08-20,50.75 1695 | 2017-08-21,59.61 1696 | 2017-08-22,60.3 1697 | 2017-08-23,58.92 1698 | 2017-08-24,57.81 1699 | 2017-08-25,51.58 1700 | 2017-08-26,52.54 1701 | 2017-08-27,47.92 1702 | 2017-08-28,47.26 1703 | 2017-08-29,54.65 1704 | 2017-08-30,55.62 1705 | 2017-08-31,62.22 1706 | 2017-09-01,60.64 1707 | 2017-09-02,56.74 1708 | 2017-09-03,53.81 1709 | 2017-09-04,50.08 1710 | 2017-09-05,43.34 1711 | 2017-09-06,58.7 1712 | 2017-09-07,60.24 1713 | 2017-09-08,50.83 1714 | 2017-09-09,48.64 1715 | 2017-09-10,58.87 1716 | 2017-09-11,54.61 1717 | 2017-09-12,53.58 1718 | 2017-09-13,56.65 1719 | 2017-09-14,48.27 1720 | 2017-09-15,48.46 1721 | 2017-09-16,63.68 1722 | 2017-09-17,62.64 1723 | 2017-09-18,51.75 1724 | 2017-09-19,51.1 1725 | 2017-09-20,53.09 1726 | 2017-09-21,62.21 1727 | 2017-09-22,53.59 1728 | 2017-09-23,49.06 1729 | 2017-09-24,58.11 1730 | 2017-09-25,52.14 1731 | 2017-09-26,50.33 1732 | 2017-09-27,53.09 1733 | 2017-09-28,56.39 1734 | 2017-09-29,53.21 1735 | 2017-09-30,52.25 1736 | 2017-10-01,53.55 1737 | 2017-10-02,50.93 1738 | 2017-10-03,48.46 1739 | 2017-10-04,57.45 1740 | 2017-10-05,60.87 1741 | 2017-10-06,56.1 1742 | 2017-10-07,50.43 1743 | 2017-10-08,53.89 1744 | 2017-10-09,57.07 1745 | 2017-10-10,55.3 1746 | 2017-10-11,40.58 1747 | 2017-10-12,41.4 1748 | 2017-10-13,57.46 1749 | 2017-10-14,59.98 1750 | 2017-10-15,52.4 1751 | 2017-10-16,60.61 1752 | 2017-10-17,67.12 1753 | 2017-10-18,55.03 1754 | 2017-10-19,51.05 1755 | 2017-10-20,61.9 1756 | 2017-10-21,69.61 1757 | 2017-10-22,66.58 1758 | 2017-10-23,60.11 1759 | 2017-10-24,48.08 1760 | 2017-10-25,56.84 1761 | 2017-10-26,63.21 1762 | 2017-10-27,52.51 1763 | 2017-10-28,57.72 1764 | 2017-10-29,59.07 1765 | 2017-10-30,58.42 1766 | 2017-10-31,58.13 1767 | 2017-11-01,57.98 1768 | 2017-11-02,56.98 1769 | 2017-11-03,57.54 1770 | 2017-11-04,58.99 1771 | 2017-11-05,56.78 1772 | 2017-11-06,57.3 1773 | 2017-11-07,47.54 1774 | 2017-11-08,47.85 1775 | 2017-11-09,41.17 1776 | 2017-11-10,51.47 1777 | 2017-11-11,54.96 1778 | 2017-11-12,61.42 1779 | 2017-11-13,64.44 1780 | 2017-11-14,55.62 1781 | 2017-11-15,59.16 1782 | 2017-11-16,56.64 1783 | 2017-11-17,61.87 1784 | 2017-11-18,61.46 1785 | 2017-11-19,61.07 1786 | 2017-11-20,58.92 1787 | 2017-11-21,69.0 1788 | 2017-11-22,65.2 1789 | 2017-11-23,61.15 1790 | 2017-11-24,59.54 1791 | 2017-11-25,67.97 1792 | 2017-11-26,73.06 1793 | 2017-11-27,75.21 1794 | 2017-11-28,70.4 1795 | 2017-11-29,63.22 1796 | 2017-11-30,59.33 1797 | 2017-12-01,53.96 1798 | 2017-12-02,62.08 1799 | 2017-12-03,77.38 1800 | 2017-12-04,75.51 1801 | 2017-12-05,58.78 1802 | 2017-12-06,56.91 1803 | 2017-12-07,59.63 1804 | 2017-12-08,67.26 1805 | 2017-12-09,81.68 1806 | 2017-12-10,81.9 1807 | 2017-12-11,81.53 1808 | 2017-12-12,71.15 1809 | 2017-12-13,67.65 1810 | 2017-12-14,60.7 1811 | 2017-12-15,61.44 1812 | 2017-12-16,63.52 1813 | 2017-12-17,68.86 1814 | 2017-12-18,75.01 1815 | 2017-12-19,69.12 1816 | 2017-12-20,65.15 1817 | 2017-12-21,65.34 1818 | 2017-12-22,58.8 1819 | 2017-12-23,64.56 1820 | 2017-12-24,66.14 1821 | 2017-12-25,68.95 1822 | 2017-12-26,71.22 1823 | 2017-12-27,74.76 1824 | 2017-12-28,73.35 1825 | 2017-12-29,76.19 1826 | 2017-12-30,74.53 1827 | 2017-12-31,73.28 1828 | 2018-01-01,72.96 1829 | 2018-01-02,76.04 1830 | 2018-01-03,77.27 1831 | 2018-01-04,68.72 1832 | 2018-01-05,77.36 1833 | 2018-01-06,75.42 1834 | 2018-01-07,83.55 1835 | 2018-01-08,92.95 1836 | 2018-01-09,80.71 1837 | 2018-01-10,75.62 1838 | 2018-01-11,78.06 1839 | 2018-01-12,78.91 1840 | 2018-01-13,77.99 1841 | 2018-01-14,84.01 1842 | 2018-01-15,79.78 1843 | 2018-01-16,76.82 1844 | 2018-01-17,70.03 1845 | 2018-01-18,75.37 1846 | 2018-01-19,80.52 1847 | 2018-01-20,80.61 1848 | 2018-01-21,80.96 1849 | 2018-01-22,86.91 1850 | 2018-01-23,85.31 1851 | 2018-01-24,84.41 1852 | 2018-01-25,81.62 1853 | 2018-01-26,80.52 1854 | 2018-01-27,84.76 1855 | 2018-01-28,84.44 1856 | 2018-01-29,87.19 1857 | 2018-01-30,94.68 1858 | 2018-01-31,94.32 1859 | 2018-02-01,93.26 1860 | 2018-02-02,85.69 1861 | 2018-02-03,83.31 1862 | 2018-02-04,88.54 1863 | 2018-02-05,89.74 1864 | 2018-02-06,85.24 1865 | 2018-02-07,87.27 1866 | 2018-02-08,98.06 1867 | 2018-02-09,98.89 1868 | 2018-02-10,92.19 1869 | 2018-02-11,90.82 1870 | 2018-02-12,98.27 1871 | 2018-02-13,100.84 1872 | 2018-02-14,96.64 1873 | 2018-02-15,96.59 1874 | 2018-02-16,100.06 1875 | 2018-02-17,100.26 1876 | 2018-02-18,90.4 1877 | 2018-02-19,95.25 1878 | 2018-02-20,95.62 1879 | 2018-02-21,94.24 1880 | 2018-02-22,91.28 1881 | 2018-02-23,91.69 1882 | 2018-02-24,96.25 1883 | 2018-02-25,103.87 1884 | 2018-02-26,99.98 1885 | 2018-02-27,93.91 1886 | 2018-02-28,103.61 1887 | 2018-03-01,111.3 1888 | 2018-03-02,108.76 1889 | 2018-03-03,92.65 1890 | 2018-03-04,89.2 1891 | 2018-03-05,92.47 1892 | 2018-03-06,91.93 1893 | 2018-03-07,91.14 1894 | 2018-03-08,91.01 1895 | 2018-03-09,92.26 1896 | 2018-03-10,99.5 1897 | 2018-03-11,98.77 1898 | 2018-03-12,94.79 1899 | 2018-03-13,91.65 1900 | 2018-03-14,90.39 1901 | 2018-03-15,84.29 1902 | 2018-03-16,89.74 1903 | 2018-03-17,99.41 1904 | 2018-03-18,95.89 1905 | 2018-03-19,93.05 1906 | 2018-03-20,97.96 1907 | 2018-03-21,94.48 1908 | 2018-03-22,92.18 1909 | 2018-03-23,88.5 1910 | 2018-03-24,97.85 1911 | 2018-03-25,104.92 1912 | 2018-03-26,103.82 1913 | 2018-03-27,98.15 1914 | 2018-03-28,96.44 1915 | 2018-03-29,92.43 1916 | 2018-03-30,88.28 1917 | 2018-03-31,97.11 1918 | 2018-04-01,99.16 1919 | 2018-04-02,96.19 1920 | 2018-04-03,92.7 1921 | 2018-04-04,83.34 1922 | 2018-04-05,88.49 1923 | 2018-04-06,93.59 1924 | 2018-04-07,89.29 1925 | 2018-04-08,86.37 1926 | 2018-04-09,89.58 1927 | 2018-04-10,89.24 1928 | 2018-04-11,96.4 1929 | 2018-04-12,100.43 1930 | 2018-04-13,97.8 1931 | 2018-04-14,87.31 1932 | 2018-04-15,93.48 1933 | 2018-04-16,106.9 1934 | 2018-04-17,96.98 1935 | 2018-04-18,96.31 1936 | 2018-04-19,103.3 1937 | 2018-04-20,103.45 1938 | 2018-04-21,86.28 1939 | 2018-04-22,94.62 1940 | 2018-04-23,100.74 1941 | 2018-04-24,98.82 1942 | 2018-04-25,103.12 1943 | 2018-04-26,96.83 1944 | 2018-04-27,103.55 1945 | 2018-04-28,105.81 1946 | 2018-04-29,101.07 1947 | 2018-04-30,95.31 1948 | 2018-05-01,92.52 1949 | 2018-05-02,84.48 1950 | 2018-05-03,83.56 1951 | 2018-05-04,81.48 1952 | 2018-05-05,80.82 1953 | 2018-05-06,93.65 1954 | 2018-05-07,99.99 1955 | 2018-05-08,95.98 1956 | 2018-05-09,93.31 1957 | 2018-05-10,85.01 1958 | 2018-05-11,86.71 1959 | 2018-05-12,96.49 1960 | 2018-05-13,89.05 1961 | 2018-05-14,97.3 1962 | 2018-05-15,91.09 1963 | 2018-05-16,87.88 1964 | 2018-05-17,89.78 1965 | 2018-05-18,89.77 1966 | 2018-05-19,91.89 1967 | 2018-05-20,93.84 1968 | 2018-05-21,92.29 1969 | 2018-05-22,80.58 1970 | 2018-05-23,71.9 1971 | 2018-05-24,76.87 1972 | 2018-05-25,84.86 1973 | 2018-05-26,85.3 1974 | 2018-05-27,90.73 1975 | 2018-05-28,92.73 1976 | 2018-05-29,89.47 1977 | 2018-05-30,92.57 1978 | 2018-05-31,75.0 1979 | 2018-06-01,72.6 1980 | 2018-06-02,86.99 1981 | 2018-06-03,90.02 1982 | 2018-06-04,91.02 1983 | 2018-06-05,85.36 1984 | 2018-06-06,86.66 1985 | 2018-06-07,84.13 1986 | 2018-06-08,86.27 1987 | 2018-06-09,89.02 1988 | 2018-06-10,80.98 1989 | 2018-06-11,84.04 1990 | 2018-06-12,85.88 1991 | 2018-06-13,85.53 1992 | 2018-06-14,97.68 1993 | 2018-06-15,96.94 1994 | 2018-06-16,85.47 1995 | 2018-06-17,90.09 1996 | 2018-06-18,89.81 1997 | 2018-06-19,77.21 1998 | 2018-06-20,73.79 1999 | 2018-06-21,76.8 2000 | 2018-06-22,84.73 2001 | 2018-06-23,86.86 2002 | 2018-06-24,79.5 2003 | 2018-06-25,74.54 2004 | 2018-06-26,75.21 2005 | 2018-06-27,75.73 2006 | 2018-06-28,81.17 2007 | 2018-06-29,76.07 2008 | 2018-06-30,72.36 2009 | 2018-07-01,72.01 2010 | 2018-07-02,82.75 2011 | 2018-07-03,82.54 2012 | 2018-07-04,84.15 2013 | 2018-07-05,82.27 2014 | 2018-07-06,77.15 2015 | 2018-07-07,72.45 2016 | 2018-07-08,83.61 2017 | 2018-07-09,85.18 2018 | 2018-07-10,85.13 2019 | 2018-07-11,89.92 2020 | 2018-07-12,79.74 2021 | 2018-07-13,80.16 2022 | 2018-07-14,76.07 2023 | 2018-07-15,74.02 2024 | 2018-07-16,73.11 2025 | 2018-07-17,73.24 2026 | 2018-07-18,72.53 2027 | 2018-07-19,69.04 2028 | 2018-07-20,70.08 2029 | 2018-07-21,71.88 2030 | 2018-07-22,75.69 2031 | 2018-07-23,83.33 2032 | 2018-07-24,81.43 2033 | 2018-07-25,73.94 2034 | 2018-07-26,71.2 2035 | 2018-07-27,70.56 2036 | 2018-07-28,71.46 2037 | 2018-07-29,72.76 2038 | 2018-07-30,64.59 2039 | 2018-07-31,69.84 2040 | 2018-08-01,73.63 2041 | 2018-08-02,69.91 2042 | 2018-08-03,61.06 2043 | 2018-08-04,68.93 2044 | 2018-08-05,71.2 2045 | 2018-08-06,66.1 2046 | 2018-08-07,60.45 2047 | 2018-08-08,71.08 2048 | 2018-08-09,74.92 2049 | 2018-08-10,57.32 2050 | 2018-08-11,52.35 2051 | 2018-08-12,60.91 2052 | 2018-08-13,65.53 2053 | 2018-08-14,64.96 2054 | 2018-08-15,57.47 2055 | 2018-08-16,57.98 2056 | 2018-08-17,58.72 2057 | 2018-08-18,64.07 2058 | 2018-08-19,68.18 2059 | 2018-08-20,58.5 2060 | 2018-08-21,64.99 2061 | 2018-08-22,68.36 2062 | 2018-08-23,59.97 2063 | 2018-08-24,65.15 2064 | 2018-08-25,59.92 2065 | 2018-08-26,58.61 2066 | 2018-08-27,64.06 2067 | 2018-08-28,68.78 2068 | 2018-08-29,69.37 2069 | 2018-08-30,55.54 2070 | 2018-08-31,49.06 2071 | 2018-09-01,54.62 2072 | 2018-09-02,65.17 2073 | 2018-09-03,61.89 2074 | 2018-09-04,61.82 2075 | 2018-09-05,62.65 2076 | 2018-09-06,51.42 2077 | 2018-09-07,54.23 2078 | 2018-09-08,60.45 2079 | 2018-09-09,57.62 2080 | 2018-09-10,57.91 2081 | 2018-09-11,60.75 2082 | 2018-09-12,66.73 2083 | 2018-09-13,65.89 2084 | 2018-09-14,53.17 2085 | 2018-09-15,53.9 2086 | 2018-09-16,57.74 2087 | 2018-09-17,64.69 2088 | 2018-09-18,68.98 2089 | 2018-09-19,61.1 2090 | 2018-09-20,70.38 2091 | 2018-09-21,63.02 2092 | 2018-09-22,52.62 2093 | 2018-09-23,52.93 2094 | 2018-09-24,56.11 2095 | 2018-09-25,66.2 2096 | 2018-09-26,69.43 2097 | 2018-09-27,60.64 2098 | 2018-09-28,53.0 2099 | 2018-09-29,55.62 2100 | 2018-09-30,64.94 2101 | 2018-10-01,62.95 2102 | 2018-10-02,52.72 2103 | 2018-10-03,56.27 2104 | 2018-10-04,60.9 2105 | 2018-10-05,61.6 2106 | 2018-10-06,54.98 2107 | 2018-10-07,49.58 2108 | 2018-10-08,53.58 2109 | 2018-10-09,55.3 2110 | 2018-10-10,47.37 2111 | 2018-10-11,50.89 2112 | 2018-10-12,50.09 2113 | 2018-10-13,43.83 2114 | 2018-10-14,50.26 2115 | 2018-10-15,51.8 2116 | 2018-10-16,50.05 2117 | 2018-10-17,60.18 2118 | 2018-10-18,70.48 2119 | 2018-10-19,63.31 2120 | 2018-10-20,61.66 2121 | 2018-10-21,61.21 2122 | 2018-10-22,64.87 2123 | 2018-10-23,64.92 2124 | 2018-10-24,60.94 2125 | 2018-10-25,61.54 2126 | 2018-10-26,59.01 2127 | 2018-10-27,69.25 2128 | 2018-10-28,61.96 2129 | 2018-10-29,55.72 2130 | 2018-10-30,63.72 2131 | 2018-10-31,61.42 2132 | 2018-11-01,58.73 2133 | 2018-11-02,60.4 2134 | 2018-11-03,60.29 2135 | 2018-11-04,54.59 2136 | 2018-11-05,55.17 2137 | 2018-11-06,58.53 2138 | 2018-11-07,57.71 2139 | 2018-11-08,67.73 2140 | 2018-11-09,67.35 2141 | 2018-11-10,62.8 2142 | 2018-11-11,69.18 2143 | 2018-11-12,70.38 2144 | 2018-11-13,64.67 2145 | 2018-11-14,59.77 2146 | 2018-11-15,58.72 2147 | 2018-11-16,68.41 2148 | 2018-11-17,61.06 2149 | 2018-11-18,52.07 2150 | 2018-11-19,61.45 2151 | 2018-11-20,73.2 2152 | 2018-11-21,73.69 2153 | 2018-11-22,63.85 2154 | 2018-11-23,66.78 2155 | 2018-11-24,70.91 2156 | 2018-11-25,71.68 2157 | 2018-11-26,68.47 2158 | 2018-11-27,62.84 2159 | 2018-11-28,64.28 2160 | 2018-11-29,79.31 2161 | 2018-11-30,85.14 2162 | 2018-12-01,70.29 2163 | 2018-12-02,66.56 2164 | 2018-12-03,65.77 2165 | 2018-12-04,71.0 2166 | 2018-12-05,74.85 2167 | 2018-12-06,71.6 2168 | 2018-12-07,65.18 2169 | 2018-12-08,63.33 2170 | 2018-12-09,76.71 2171 | 2018-12-10,74.88 2172 | 2018-12-11,80.35 2173 | 2018-12-12,73.77 2174 | 2018-12-13,72.17 2175 | 2018-12-14,81.91 2176 | 2018-12-15,72.25 2177 | 2018-12-16,74.5 2178 | 2018-12-17,75.96 2179 | 2018-12-18,65.27 2180 | 2018-12-19,59.88 2181 | 2018-12-20,79.08 2182 | 2018-12-21,82.44 2183 | 2018-12-22,78.63 2184 | 2018-12-23,87.08 2185 | 2018-12-24,80.9 2186 | 2018-12-25,70.75 2187 | 2018-12-26,73.79 2188 | 2018-12-27,72.91 2189 | 2018-12-28,84.96 2190 | 2018-12-29,87.43 2191 | 2018-12-30,86.25 2192 | 2018-12-31,86.66 2193 | 2019-01-01,71.16 2194 | 2019-01-02,73.6 2195 | 2019-01-03,80.44 2196 | 2019-01-04,82.17 2197 | 2019-01-05,80.94 2198 | 2019-01-06,84.49 2199 | 2019-01-07,85.16 2200 | 2019-01-08,77.25 2201 | 2019-01-09,75.29 2202 | 2019-01-10,84.56 2203 | 2019-01-11,96.77 2204 | 2019-01-12,87.44 2205 | 2019-01-13,78.22 2206 | 2019-01-14,79.01 2207 | 2019-01-15,86.35 2208 | 2019-01-16,86.07 2209 | 2019-01-17,76.4 2210 | 2019-01-18,85.14 2211 | 2019-01-19,93.0 2212 | 2019-01-20,90.53 2213 | 2019-01-21,90.79 2214 | 2019-01-22,98.54 2215 | 2019-01-23,89.6 2216 | 2019-01-24,81.68 2217 | 2019-01-25,85.42 2218 | 2019-01-26,87.13 2219 | 2019-01-27,99.91 2220 | 2019-01-28,102.32 2221 | 2019-01-29,91.22 2222 | 2019-01-30,85.78 2223 | 2019-01-31,82.79 2224 | 2019-02-01,85.96 2225 | 2019-02-02,101.07 2226 | 2019-02-03,108.98 2227 | 2019-02-04,103.55 2228 | 2019-02-05,98.01 2229 | 2019-02-06,97.92 2230 | 2019-02-07,96.71 2231 | 2019-02-08,89.89 2232 | 2019-02-09,94.82 2233 | 2019-02-10,102.99 2234 | 2019-02-11,103.32 2235 | 2019-02-12,98.18 2236 | 2019-02-13,87.79 2237 | 2019-02-14,94.28 2238 | 2019-02-15,102.22 2239 | 2019-02-16,99.72 2240 | 2019-02-17,103.63 2241 | 2019-02-18,107.6 2242 | 2019-02-19,95.84 2243 | 2019-02-20,90.15 2244 | 2019-02-21,90.09 2245 | 2019-02-22,91.33 2246 | 2019-02-23,93.49 2247 | 2019-02-24,94.07 2248 | 2019-02-25,83.8 2249 | 2019-02-26,78.57 2250 | 2019-02-27,89.48 2251 | 2019-02-28,89.53 2252 | 2019-03-01,87.79 2253 | 2019-03-02,90.73 2254 | 2019-03-03,97.06 2255 | 2019-03-04,97.39 2256 | 2019-03-05,89.13 2257 | 2019-03-06,92.0 2258 | 2019-03-07,96.65 2259 | 2019-03-08,100.5 2260 | 2019-03-09,94.91 2261 | 2019-03-10,97.16 2262 | 2019-03-11,101.67 2263 | 2019-03-12,98.36 2264 | 2019-03-13,97.35 2265 | 2019-03-14,101.04 2266 | 2019-03-15,117.46 2267 | 2019-03-16,111.78 2268 | 2019-03-17,97.45 2269 | 2019-03-18,103.64 2270 | 2019-03-19,108.16 2271 | 2019-03-20,113.16 2272 | 2019-03-21,103.89 2273 | 2019-03-22,91.73 2274 | 2019-03-23,96.46 2275 | 2019-03-24,104.7 2276 | 2019-03-25,106.23 2277 | 2019-03-26,99.26 2278 | 2019-03-27,94.37 2279 | 2019-03-28,103.36 2280 | 2019-03-29,104.65 2281 | 2019-03-30,94.53 2282 | 2019-03-31,89.0 2283 | 2019-04-01,91.03 2284 | 2019-04-02,98.89 2285 | 2019-04-03,111.46 2286 | 2019-04-04,98.32 2287 | 2019-04-05,102.14 2288 | 2019-04-06,103.47 2289 | 2019-04-07,98.2 2290 | 2019-04-08,101.79 2291 | 2019-04-09,98.41 2292 | 2019-04-10,103.08 2293 | 2019-04-11,105.46 2294 | 2019-04-12,105.17 2295 | 2019-04-13,105.79 2296 | 2019-04-14,102.17 2297 | 2019-04-15,98.89 2298 | 2019-04-16,109.9 2299 | 2019-04-17,113.13 2300 | 2019-04-18,98.92 2301 | 2019-04-19,96.12 2302 | 2019-04-20,110.15 2303 | 2019-04-21,102.81 2304 | 2019-04-22,100.63 2305 | 2019-04-23,103.66 2306 | 2019-04-24,98.06 2307 | 2019-04-25,100.8 2308 | 2019-04-26,93.41 2309 | 2019-04-27,96.06 2310 | 2019-04-28,94.72 2311 | 2019-04-29,98.11 2312 | 2019-04-30,96.97 2313 | 2019-05-01,89.15 2314 | 2019-05-02,102.73 2315 | 2019-05-03,106.06 2316 | 2019-05-04,100.32 2317 | 2019-05-05,94.35 2318 | 2019-05-06,92.56 2319 | 2019-05-07,92.94 2320 | 2019-05-08,105.63 2321 | 2019-05-09,109.94 2322 | 2019-05-10,99.63 2323 | 2019-05-11,99.55 2324 | 2019-05-12,96.89 2325 | 2019-05-13,94.25 2326 | 2019-05-14,101.45 2327 | 2019-05-15,99.62 2328 | 2019-05-16,103.39 2329 | 2019-05-17,109.82 2330 | 2019-05-18,101.65 2331 | 2019-05-19,96.48 2332 | 2019-05-20,98.98 2333 | 2019-05-21,91.95 2334 | 2019-05-22,95.42 2335 | 2019-05-23,94.68 2336 | 2019-05-24,91.39 2337 | 2019-05-25,107.48 2338 | 2019-05-26,100.97 2339 | 2019-05-27,96.89 2340 | 2019-05-28,98.81 2341 | 2019-05-29,89.06 2342 | 2019-05-30,80.12 2343 | 2019-05-31,89.0 2344 | 2019-06-01,86.64 2345 | 2019-06-02,80.56 2346 | 2019-06-03,88.27 2347 | 2019-06-04,92.43 2348 | 2019-06-05,88.7 2349 | 2019-06-06,91.21 2350 | 2019-06-07,88.15 2351 | 2019-06-08,83.63 2352 | 2019-06-09,93.19 2353 | 2019-06-10,81.36 2354 | 2019-06-11,83.83 2355 | 2019-06-12,86.61 2356 | 2019-06-13,84.67 2357 | 2019-06-14,92.41 2358 | 2019-06-15,96.3 2359 | 2019-06-16,95.86 2360 | 2019-06-17,92.23 2361 | 2019-06-18,90.47 2362 | 2019-06-19,78.01 2363 | 2019-06-20,77.35 2364 | 2019-06-21,89.68 2365 | 2019-06-22,86.68 2366 | 2019-06-23,84.26 2367 | 2019-06-24,96.56 2368 | 2019-06-25,96.77 2369 | 2019-06-26,85.38 2370 | 2019-06-27,75.47 2371 | 2019-06-28,78.36 2372 | 2019-06-29,83.63 2373 | 2019-06-30,80.89 2374 | 2019-07-01,85.31 2375 | 2019-07-02,81.58 2376 | 2019-07-03,71.7 2377 | 2019-07-04,75.4 2378 | 2019-07-05,81.71 2379 | 2019-07-06,82.13 2380 | 2019-07-07,84.94 2381 | 2019-07-08,78.68 2382 | 2019-07-09,80.7 2383 | 2019-07-10,76.66 2384 | 2019-07-11,76.34 2385 | 2019-07-12,82.03 2386 | 2019-07-13,87.51 2387 | 2019-07-14,81.69 2388 | 2019-07-15,65.38 2389 | 2019-07-16,79.48 2390 | 2019-07-17,96.15 2391 | 2019-07-18,80.72 2392 | 2019-07-19,75.63 2393 | 2019-07-20,82.94 2394 | 2019-07-21,74.83 2395 | 2019-07-22,71.11 2396 | 2019-07-23,71.53 2397 | 2019-07-24,69.88 2398 | 2019-07-25,75.44 2399 | 2019-07-26,79.57 2400 | 2019-07-27,73.75 2401 | 2019-07-28,80.2 2402 | 2019-07-29,78.34 2403 | 2019-07-30,82.66 2404 | 2019-07-31,80.33 2405 | 2019-08-01,79.27 2406 | 2019-08-02,81.77 2407 | 2019-08-03,86.64 2408 | 2019-08-04,73.51 2409 | 2019-08-05,60.72 2410 | 2019-08-06,63.84 2411 | 2019-08-07,71.58 2412 | 2019-08-08,66.27 2413 | 2019-08-09,66.99 2414 | 2019-08-10,76.42 2415 | 2019-08-11,69.14 2416 | 2019-08-12,71.02 2417 | 2019-08-13,65.47 2418 | 2019-08-14,61.41 2419 | 2019-08-15,69.85 2420 | 2019-08-16,67.02 2421 | 2019-08-17,71.42 2422 | 2019-08-18,70.93 2423 | 2019-08-19,68.07 2424 | 2019-08-20,62.59 2425 | 2019-08-21,63.28 2426 | 2019-08-22,61.52 2427 | 2019-08-23,60.11 2428 | 2019-08-24,66.63 2429 | 2019-08-25,63.75 2430 | 2019-08-26,64.03 2431 | 2019-08-27,74.37 2432 | 2019-08-28,72.5 2433 | 2019-08-29,62.66 2434 | 2019-08-30,72.56 2435 | 2019-08-31,77.57 2436 | 2019-09-01,71.46 2437 | 2019-09-02,61.18 2438 | 2019-09-03,59.81 2439 | 2019-09-04,68.12 2440 | 2019-09-05,76.97 2441 | 2019-09-06,66.34 2442 | 2019-09-07,61.98 2443 | 2019-09-08,58.88 2444 | 2019-09-09,57.52 2445 | 2019-09-10,63.19 2446 | 2019-09-11,57.4 2447 | 2019-09-12,67.86 2448 | 2019-09-13,66.07 2449 | 2019-09-14,66.75 2450 | 2019-09-15,71.54 2451 | 2019-09-16,72.52 2452 | 2019-09-17,69.18 2453 | 2019-09-18,68.95 2454 | 2019-09-19,61.25 2455 | 2019-09-20,56.19 2456 | 2019-09-21,64.92 2457 | 2019-09-22,70.04 2458 | 2019-09-23,70.25 2459 | 2019-09-24,65.99 2460 | 2019-09-25,62.2 2461 | 2019-09-26,63.92 2462 | 2019-09-27,63.03 2463 | 2019-09-28,66.09 2464 | 2019-09-29,65.91 2465 | 2019-09-30,65.07 2466 | 2019-10-01,55.15 2467 | 2019-10-02,57.22 2468 | 2019-10-03,65.4 2469 | 2019-10-04,68.39 2470 | 2019-10-05,66.32 2471 | 2019-10-06,63.23 2472 | 2019-10-07,65.99 2473 | 2019-10-08,66.08 2474 | 2019-10-09,66.84 2475 | 2019-10-10,74.1 2476 | 2019-10-11,66.47 2477 | 2019-10-12,60.9 2478 | 2019-10-13,57.5 2479 | 2019-10-14,59.95 2480 | 2019-10-15,64.31 2481 | 2019-10-16,72.29 2482 | 2019-10-17,68.37 2483 | 2019-10-18,66.02 2484 | 2019-10-19,69.47 2485 | 2019-10-20,61.31 2486 | 2019-10-21,62.44 2487 | 2019-10-22,68.44 2488 | 2019-10-23,81.27 2489 | 2019-10-24,75.04 2490 | 2019-10-25,67.0 2491 | 2019-10-26,71.52 2492 | 2019-10-27,73.58 2493 | 2019-10-28,74.35 2494 | 2019-10-29,74.33 2495 | 2019-10-30,66.57 2496 | 2019-10-31,67.21 2497 | 2019-11-01,64.04 2498 | 2019-11-02,65.19 2499 | 2019-11-03,76.59 2500 | 2019-11-04,72.86 2501 | 2019-11-05,73.7 2502 | 2019-11-06,74.25 2503 | 2019-11-07,70.74 2504 | 2019-11-08,68.35 2505 | 2019-11-09,77.45 2506 | 2019-11-10,72.26 2507 | 2019-11-11,70.48 2508 | 2019-11-12,74.86 2509 | 2019-11-13,83.21 2510 | 2019-11-14,85.6 2511 | 2019-11-15,86.06 2512 | 2019-11-16,77.95 2513 | 2019-11-17,71.11 2514 | 2019-11-18,77.78 2515 | 2019-11-19,77.54 2516 | 2019-11-20,75.87 2517 | 2019-11-21,79.71 2518 | 2019-11-22,73.26 2519 | 2019-11-23,65.65 2520 | 2019-11-24,63.21 2521 | 2019-11-25,63.83 2522 | 2019-11-26,72.57 2523 | 2019-11-27,79.17 2524 | 2019-11-28,81.92 2525 | 2019-11-29,81.48 2526 | 2019-11-30,77.39 2527 | 2019-12-01,66.14 2528 | 2019-12-02,68.68 2529 | 2019-12-03,81.73 2530 | 2019-12-04,69.66 2531 | 2019-12-05,71.87 2532 | 2019-12-06,86.71 2533 | 2019-12-07,85.41 2534 | 2019-12-08,72.49 2535 | 2019-12-09,75.42 2536 | 2019-12-10,68.49 2537 | 2019-12-11,84.73 2538 | 2019-12-12,80.56 2539 | 2019-12-13,74.42 2540 | 2019-12-14,83.42 2541 | 2019-12-15,75.39 2542 | 2019-12-16,77.03 2543 | 2019-12-17,88.61 2544 | 2019-12-18,89.78 2545 | 2019-12-19,83.82 2546 | 2019-12-20,79.12 2547 | 2019-12-21,75.56 2548 | 2019-12-22,84.66 2549 | 2019-12-23,97.06 2550 | 2019-12-24,92.44 2551 | 2019-12-25,79.34 2552 | 2019-12-26,78.63 2553 | 2019-12-27,94.48 2554 | 2019-12-28,95.08 2555 | 2019-12-29,77.18 2556 | 2019-12-30,79.32 2557 | 2019-12-31,84.07 2558 | 2020-01-01,75.44 2559 | 2020-01-02,79.72 2560 | 2020-01-03,89.62 2561 | 2020-01-04,92.61 2562 | 2020-01-05,89.18 2563 | 2020-01-06,94.32 2564 | 2020-01-07,85.49 2565 | 2020-01-08,79.26 2566 | 2020-01-09,78.69 2567 | 2020-01-10,89.61 2568 | 2020-01-11,82.44 2569 | 2020-01-12,76.86 2570 | 2020-01-13,89.13 2571 | 2020-01-14,84.85 2572 | 2020-01-15,84.35 2573 | 2020-01-16,92.76 2574 | 2020-01-17,102.52 2575 | 2020-01-18,97.15 2576 | 2020-01-19,96.0 2577 | 2020-01-20,94.44 2578 | 2020-01-21,89.32 2579 | 2020-01-22,84.25 2580 | 2020-01-23,88.76 2581 | 2020-01-24,97.68 2582 | 2020-01-25,94.59 2583 | 2020-01-26,85.02 2584 | 2020-01-27,84.95 2585 | 2020-01-28,89.14 2586 | 2020-01-29,84.75 2587 | 2020-01-30,89.02 2588 | 2020-01-31,95.72 2589 | 2020-02-01,96.36 2590 | 2020-02-02,98.57 2591 | 2020-02-03,91.67 2592 | 2020-02-04,90.55 2593 | 2020-02-05,102.45 2594 | 2020-02-06,107.88 2595 | 2020-02-07,105.2 2596 | 2020-02-08,96.16 2597 | 2020-02-09,98.03 2598 | 2020-02-10,101.05 2599 | 2020-02-11,94.5 2600 | 2020-02-12,94.77 2601 | 2020-02-13,106.67 2602 | 2020-02-14,110.97 2603 | 2020-02-15,110.18 2604 | 2020-02-16,109.37 2605 | 2020-02-17,111.64 2606 | 2020-02-18,100.08 2607 | 2020-02-19,95.5 2608 | 2020-02-20,93.0 2609 | 2020-02-21,96.53 2610 | 2020-02-22,104.08 2611 | 2020-02-23,102.54 2612 | 2020-02-24,114.12 2613 | 2020-02-25,109.07 2614 | 2020-02-26,99.94 2615 | 2020-02-27,106.1 2616 | 2020-02-28,101.72 2617 | 2020-02-29,98.96 2618 | 2020-03-01,100.67 2619 | 2020-03-02,112.64 2620 | 2020-03-03,111.83 2621 | 2020-03-04,104.83 2622 | 2020-03-05,102.57 2623 | 2020-03-06,84.44 2624 | 2020-03-07,88.96 2625 | 2020-03-08,106.79 2626 | 2020-03-09,108.65 2627 | 2020-03-10,111.92 2628 | 2020-03-11,107.79 2629 | 2020-03-12,104.67 2630 | 2020-03-13,111.45 2631 | 2020-03-14,109.9 2632 | 2020-03-15,97.16 2633 | 2020-03-16,95.39 2634 | 2020-03-17,98.06 2635 | 2020-03-18,107.33 2636 | 2020-03-19,110.57 2637 | 2020-03-20,111.2 2638 | 2020-03-21,121.26 2639 | 2020-03-22,112.01 2640 | 2020-03-23,100.9 2641 | 2020-03-24,104.9 2642 | 2020-03-25,105.49 2643 | 2020-03-26,109.72 2644 | 2020-03-27,107.43 2645 | 2020-03-28,97.33 2646 | 2020-03-29,103.4 2647 | 2020-03-30,110.75 2648 | 2020-03-31,108.38 2649 | 2020-04-01,108.74 2650 | 2020-04-02,106.71 2651 | 2020-04-03,104.72 2652 | 2020-04-04,112.23 2653 | 2020-04-05,111.68 2654 | 2020-04-06,107.55 2655 | 2020-04-07,105.71 2656 | 2020-04-08,108.83 2657 | 2020-04-09,103.41 2658 | 2020-04-10,105.4 2659 | 2020-04-11,118.41 2660 | 2020-04-12,123.29 2661 | 2020-04-13,113.52 2662 | 2020-04-14,115.0 2663 | 2020-04-15,114.42 2664 | 2020-04-16,97.5 2665 | 2020-04-17,103.94 2666 | 2020-04-18,114.24 2667 | 2020-04-19,110.89 2668 | 2020-04-20,110.69 2669 | 2020-04-21,109.05 2670 | 2020-04-22,96.54 2671 | 2020-04-23,100.47 2672 | 2020-04-24,110.48 2673 | 2020-04-25,101.71 2674 | 2020-04-26,103.9 2675 | 2020-04-27,94.9 2676 | 2020-04-28,97.28 2677 | 2020-04-29,102.51 2678 | 2020-04-30,109.99 2679 | 2020-05-01,110.57 2680 | 2020-05-02,102.4 2681 | 2020-05-03,98.88 2682 | 2020-05-04,97.28 2683 | 2020-05-05,105.94 2684 | 2020-05-06,107.47 2685 | 2020-05-07,112.2 2686 | 2020-05-08,114.19 2687 | 2020-05-09,107.93 2688 | 2020-05-10,103.48 2689 | 2020-05-11,108.1 2690 | 2020-05-12,96.63 2691 | 2020-05-13,99.16 2692 | 2020-05-14,107.52 2693 | 2020-05-15,101.99 2694 | 2020-05-16,102.95 2695 | 2020-05-17,100.63 2696 | 2020-05-18,93.36 2697 | 2020-05-19,98.38 2698 | 2020-05-20,108.98 2699 | 2020-05-21,104.1 2700 | 2020-05-22,100.0 2701 | 2020-05-23,101.45 2702 | 2020-05-24,100.61 2703 | 2020-05-25,96.94 2704 | 2020-05-26,100.93 2705 | 2020-05-27,97.71 2706 | 2020-05-28,97.36 2707 | 2020-05-29,90.93 2708 | 2020-05-30,88.77 2709 | 2020-05-31,103.99 2710 | 2020-06-01,102.44 2711 | 2020-06-02,96.93 2712 | 2020-06-03,86.23 2713 | 2020-06-04,88.56 2714 | 2020-06-05,94.12 2715 | 2020-06-06,93.31 2716 | 2020-06-07,99.65 2717 | 2020-06-08,89.56 2718 | 2020-06-09,74.07 2719 | 2020-06-10,89.58 2720 | 2020-06-11,85.43 2721 | 2020-06-12,79.41 2722 | 2020-06-13,88.53 2723 | 2020-06-14,94.16 2724 | 2020-06-15,90.56 2725 | 2020-06-16,85.74 2726 | 2020-06-17,89.17 2727 | 2020-06-18,93.72 2728 | 2020-06-19,104.63 2729 | 2020-06-20,105.18 2730 | 2020-06-21,106.18 2731 | 2020-06-22,101.65 2732 | 2020-06-23,90.89 2733 | 2020-06-24,91.09 2734 | 2020-06-25,92.31 2735 | 2020-06-26,93.76 2736 | 2020-06-27,96.78 2737 | 2020-06-28,92.35 2738 | 2020-06-29,88.09 2739 | 2020-06-30,91.88 2740 | 2020-07-01,91.44 2741 | 2020-07-02,89.64 2742 | 2020-07-03,82.06 2743 | 2020-07-04,76.15 2744 | 2020-07-05,82.95 2745 | 2020-07-06,82.64 2746 | 2020-07-07,86.39 2747 | 2020-07-08,91.81 2748 | 2020-07-09,90.02 2749 | 2020-07-10,84.9 2750 | 2020-07-11,81.34 2751 | 2020-07-12,82.3 2752 | 2020-07-13,75.19 2753 | 2020-07-14,75.45 2754 | 2020-07-15,78.24 2755 | 2020-07-16,78.49 2756 | 2020-07-17,75.75 2757 | 2020-07-18,75.4 2758 | 2020-07-19,79.06 2759 | 2020-07-20,76.28 2760 | 2020-07-21,78.92 2761 | 2020-07-22,76.81 2762 | 2020-07-23,68.27 2763 | 2020-07-24,67.71 2764 | 2020-07-25,70.19 2765 | 2020-07-26,73.32 2766 | 2020-07-27,66.73 2767 | 2020-07-28,69.44 2768 | 2020-07-29,79.24 2769 | 2020-07-30,77.82 2770 | 2020-07-31,77.55 2771 | 2020-08-01,83.08 2772 | 2020-08-02,84.19 2773 | 2020-08-03,83.78 2774 | 2020-08-04,78.4 2775 | 2020-08-05,80.33 2776 | 2020-08-06,79.78 2777 | 2020-08-07,81.32 2778 | 2020-08-08,87.83 2779 | 2020-08-09,94.79 2780 | 2020-08-10,84.41 2781 | 2020-08-11,71.22 2782 | 2020-08-12,67.17 2783 | 2020-08-13,67.56 2784 | 2020-08-14,68.64 2785 | 2020-08-15,70.21 2786 | 2020-08-16,75.31 2787 | 2020-08-17,73.75 2788 | 2020-08-18,67.63 2789 | 2020-08-19,65.87 2790 | 2020-08-20,81.22 2791 | 2020-08-21,85.22 2792 | 2020-08-22,79.78 2793 | 2020-08-23,73.87 2794 | 2020-08-24,65.16 2795 | 2020-08-25,61.39 2796 | 2020-08-26,58.81 2797 | 2020-08-27,60.88 2798 | 2020-08-28,58.62 2799 | 2020-08-29,76.61 2800 | 2020-08-30,83.33 2801 | 2020-08-31,64.44 2802 | 2020-09-01,68.19 2803 | 2020-09-02,71.97 2804 | 2020-09-03,68.09 2805 | 2020-09-04,68.29 2806 | 2020-09-05,63.78 2807 | 2020-09-06,62.8 2808 | 2020-09-07,73.54 2809 | 2020-09-08,79.34 2810 | 2020-09-09,79.8 2811 | 2020-09-10,72.9 2812 | 2020-09-11,69.93 2813 | 2020-09-12,70.12 2814 | 2020-09-13,70.53 2815 | 2020-09-14,76.2 2816 | 2020-09-15,65.28 2817 | 2020-09-16,68.42 2818 | 2020-09-17,74.3 2819 | 2020-09-18,67.96 2820 | 2020-09-19,68.82 2821 | 2020-09-20,76.27 2822 | 2020-09-21,74.62 2823 | 2020-09-22,67.25 2824 | 2020-09-23,66.66 2825 | 2020-09-24,66.84 2826 | 2020-09-25,64.5 2827 | 2020-09-26,70.07 2828 | 2020-09-27,72.38 2829 | 2020-09-28,67.19 2830 | 2020-09-29,61.89 2831 | 2020-09-30,63.64 2832 | 2020-10-01,68.3 2833 | 2020-10-02,67.27 2834 | 2020-10-03,64.11 2835 | 2020-10-04,69.28 2836 | 2020-10-05,68.88 2837 | 2020-10-06,65.39 2838 | 2020-10-07,64.62 2839 | 2020-10-08,54.97 2840 | 2020-10-09,56.02 2841 | 2020-10-10,64.21 2842 | 2020-10-11,72.72 2843 | 2020-10-12,72.11 2844 | 2020-10-13,72.0 2845 | 2020-10-14,67.99 2846 | 2020-10-15,60.19 2847 | 2020-10-16,58.27 2848 | 2020-10-17,55.57 2849 | 2020-10-18,61.31 2850 | 2020-10-19,75.05 2851 | 2020-10-20,75.0 2852 | 2020-10-21,71.95 2853 | 2020-10-22,71.85 2854 | 2020-10-23,72.66 2855 | 2020-10-24,72.39 2856 | 2020-10-25,72.9 2857 | 2020-10-26,72.33 2858 | 2020-10-27,71.13 2859 | 2020-10-28,80.91 2860 | 2020-10-29,68.36 2861 | 2020-10-30,61.84 2862 | 2020-10-31,72.54 2863 | 2020-11-01,76.79 2864 | 2020-11-02,75.8 2865 | 2020-11-03,70.29 2866 | 2020-11-04,70.08 2867 | 2020-11-05,79.82 2868 | 2020-11-06,82.08 2869 | 2020-11-07,84.2 2870 | 2020-11-08,82.96 2871 | 2020-11-09,72.59 2872 | 2020-11-10,69.36 2873 | 2020-11-11,75.82 2874 | 2020-11-12,77.61 2875 | 2020-11-13,76.73 2876 | 2020-11-14,78.66 2877 | 2020-11-15,72.57 2878 | 2020-11-16,67.57 2879 | 2020-11-17,71.86 2880 | 2020-11-18,70.47 2881 | 2020-11-19,67.44 2882 | 2020-11-20,67.61 2883 | 2020-11-21,77.62 2884 | 2020-11-22,89.74 2885 | 2020-11-23,80.55 2886 | 2020-11-24,76.97 2887 | 2020-11-25,75.67 2888 | 2020-11-26,77.38 2889 | 2020-11-27,76.76 2890 | 2020-11-28,79.79 2891 | 2020-11-29,85.35 2892 | 2020-11-30,87.39 2893 | 2020-12-01,87.05 2894 | 2020-12-02,86.21 2895 | 2020-12-03,81.83 2896 | 2020-12-04,76.12 2897 | 2020-12-05,76.12 2898 | 2020-12-06,77.53 2899 | 2020-12-07,79.58 2900 | 2020-12-08,86.29 2901 | 2020-12-09,85.59 2902 | 2020-12-10,85.19 2903 | 2020-12-11,77.08 2904 | 2020-12-12,76.39 2905 | 2020-12-13,79.35 2906 | 2020-12-14,72.69 2907 | 2020-12-15,81.46 2908 | 2020-12-16,86.27 2909 | 2020-12-17,91.31 2910 | 2020-12-18,98.72 2911 | 2020-12-19,101.55 2912 | 2020-12-20,87.34 2913 | 2020-12-21,84.11 2914 | 2020-12-22,88.59 2915 | 2020-12-23,88.21 2916 | 2020-12-24,80.7 2917 | 2020-12-25,88.05 2918 | 2020-12-26,92.15 2919 | 2020-12-27,101.16 2920 | 2020-12-28,92.59 2921 | 2020-12-29,84.02 2922 | 2020-12-30,86.14 2923 | 2020-12-31,89.81 2924 | 2021-01-01,92.12 2925 | 2021-01-02,90.97 2926 | 2021-01-03,93.43 2927 | 2021-01-04,89.58 2928 | 2021-01-05,86.28 2929 | 2021-01-06,93.98 2930 | 2021-01-07,93.93 2931 | 2021-01-08,91.81 2932 | 2021-01-09,88.81 2933 | 2021-01-10,88.68 2934 | 2021-01-11,92.63 2935 | 2021-01-12,99.3 2936 | 2021-01-13,92.59 2937 | 2021-01-14,97.06 2938 | 2021-01-15,99.28 2939 | 2021-01-16,92.61 2940 | 2021-01-17,88.63 2941 | 2021-01-18,97.76 2942 | 2021-01-19,103.73 2943 | 2021-01-20,95.29 2944 | 2021-01-21,98.13 2945 | 2021-01-22,102.14 2946 | 2021-01-23,103.07 2947 | 2021-01-24,99.03 2948 | 2021-01-25,94.93 2949 | 2021-01-26,99.87 2950 | 2021-01-27,99.87 2951 | 2021-01-28,93.51 2952 | 2021-01-29,102.05 2953 | 2021-01-30,113.9 2954 | 2021-01-31,111.03 2955 | 2021-02-01,100.58 2956 | 2021-02-02,100.1 2957 | 2021-02-03,98.39 2958 | 2021-02-04,100.23 2959 | 2021-02-05,102.96 2960 | 2021-02-06,104.36 2961 | 2021-02-07,100.42 2962 | 2021-02-08,92.83 2963 | 2021-02-09,96.54 2964 | 2021-02-10,94.74 2965 | 2021-02-11,99.78 2966 | 2021-02-12,105.67 2967 | 2021-02-13,108.68 2968 | 2021-02-14,102.88 2969 | 2021-02-15,92.08 2970 | 2021-02-16,96.11 2971 | 2021-02-17,109.23 2972 | 2021-02-18,110.85 2973 | 2021-02-19,113.71 2974 | 2021-02-20,113.46 2975 | 2021-02-21,114.65 2976 | 2021-02-22,108.13 2977 | 2021-02-23,102.44 2978 | 2021-02-24,110.33 2979 | 2021-02-25,114.71 2980 | 2021-02-26,118.75 2981 | 2021-02-27,116.72 2982 | 2021-02-28,115.44 2983 | 2021-03-01,113.48 2984 | 2021-03-02,111.13 2985 | 2021-03-03,104.76 2986 | 2021-03-04,103.7 2987 | 2021-03-05,106.97 2988 | 2021-03-06,109.46 2989 | 2021-03-07,104.88 2990 | 2021-03-08,101.13 2991 | 2021-03-09,104.2 2992 | 2021-03-10,110.66 2993 | 2021-03-11,113.72 2994 | 2021-03-12,104.82 2995 | 2021-03-13,113.46 2996 | 2021-03-14,118.54 2997 | 2021-03-15,104.83 2998 | 2021-03-16,93.98 2999 | 2021-03-17,106.45 3000 | 2021-03-18,117.46 3001 | 2021-03-19,112.2 3002 | 2021-03-20,109.35 3003 | 2021-03-21,108.74 3004 | 2021-03-22,106.23 3005 | 2021-03-23,105.7 3006 | 2021-03-24,107.29 3007 | 2021-03-25,108.96 3008 | 2021-03-26,111.49 3009 | 2021-03-27,108.08 3010 | 2021-03-28,113.82 3011 | 2021-03-29,104.78 3012 | 2021-03-30,102.74 3013 | 2021-03-31,113.35 3014 | 2021-04-01,107.84 3015 | 2021-04-02,118.65 3016 | 2021-04-03,123.05 3017 | 2021-04-04,112.27 3018 | 2021-04-05,109.21 3019 | 2021-04-06,107.81 3020 | 2021-04-07,113.76 3021 | 2021-04-08,110.1 3022 | 2021-04-09,111.94 3023 | 2021-04-10,115.9 3024 | 2021-04-11,115.81 3025 | 2021-04-12,109.57 3026 | 2021-04-13,111.11 3027 | 2021-04-14,107.1 3028 | 2021-04-15,114.13 3029 | 2021-04-16,112.71 3030 | 2021-04-17,114.11 3031 | 2021-04-18,116.13 3032 | 2021-04-19,115.99 3033 | 2021-04-20,112.17 3034 | 2021-04-21,109.04 3035 | 2021-04-22,113.28 3036 | 2021-04-23,116.72 3037 | 2021-04-24,116.25 3038 | 2021-04-25,112.83 3039 | 2021-04-26,111.26 3040 | 2021-04-27,114.7 3041 | 2021-04-28,104.57 3042 | 2021-04-29,93.93 3043 | 2021-04-30,100.78 3044 | 2021-05-01,102.28 3045 | 2021-05-02,97.35 3046 | 2021-05-03,110.74 3047 | 2021-05-04,109.96 3048 | 2021-05-05,106.92 3049 | 2021-05-06,103.78 3050 | 2021-05-07,112.05 3051 | 2021-05-08,111.45 3052 | 2021-05-09,105.35 3053 | 2021-05-10,102.94 3054 | 2021-05-11,113.78 3055 | 2021-05-12,115.83 3056 | 2021-05-13,106.58 3057 | 2021-05-14,102.68 3058 | 2021-05-15,113.56 3059 | 2021-05-16,107.95 3060 | 2021-05-17,106.5 3061 | 2021-05-18,107.43 3062 | 2021-05-19,95.82 3063 | 2021-05-20,94.93 3064 | 2021-05-21,103.55 3065 | 2021-05-22,111.34 3066 | 2021-05-23,110.34 3067 | 2021-05-24,101.85 3068 | 2021-05-25,104.99 3069 | 2021-05-26,112.3 3070 | 2021-05-27,104.79 3071 | 2021-05-28,96.96 3072 | 2021-05-29,106.5 3073 | 2021-05-30,109.17 3074 | 2021-05-31,100.0 3075 | 2021-06-01,98.86 3076 | 2021-06-02,103.52 3077 | 2021-06-03,104.28 3078 | 2021-06-04,102.19 3079 | 2021-06-05,92.23 3080 | 2021-06-06,98.0 3081 | 2021-06-07,94.15 3082 | 2021-06-08,104.19 3083 | 2021-06-09,106.83 3084 | 2021-06-10,106.86 3085 | 2021-06-11,102.57 3086 | 2021-06-12,96.93 3087 | 2021-06-13,93.12 3088 | 2021-06-14,93.08 3089 | 2021-06-15,95.15 3090 | 2021-06-16,93.31 3091 | 2021-06-17,95.35 3092 | 2021-06-18,98.39 3093 | 2021-06-19,94.58 3094 | 2021-06-20,91.07 3095 | 2021-06-21,102.47 3096 | 2021-06-22,101.55 3097 | 2021-06-23,101.36 3098 | 2021-06-24,99.82 3099 | 2021-06-25,106.35 3100 | 2021-06-26,101.65 3101 | 2021-06-27,105.78 3102 | 2021-06-28,95.9 3103 | 2021-06-29,88.9 3104 | 2021-06-30,87.66 3105 | 2021-07-01,92.16 3106 | 2021-07-02,100.14 3107 | 2021-07-03,85.94 3108 | 2021-07-04,87.81 3109 | 2021-07-05,100.42 3110 | 2021-07-06,94.59 3111 | 2021-07-07,90.36 3112 | 2021-07-08,89.32 3113 | 2021-07-09,89.9 3114 | 2021-07-10,94.81 3115 | 2021-07-11,84.05 3116 | 2021-07-12,88.39 3117 | 2021-07-13,90.44 3118 | 2021-07-14,82.72 3119 | 2021-07-15,77.5 3120 | 2021-07-16,75.64 3121 | 2021-07-17,100.0 3122 | 2021-07-18,102.67 3123 | 2021-07-19,86.71 3124 | 2021-07-20,86.12 3125 | 2021-07-21,81.1 3126 | 2021-07-22,81.71 3127 | 2021-07-23,82.12 3128 | 2021-07-24,86.5 3129 | 2021-07-25,94.31 3130 | 2021-07-26,91.71 3131 | 2021-07-27,79.17 3132 | 2021-07-28,80.93 3133 | 2021-07-29,82.27 3134 | 2021-07-30,75.41 3135 | 2021-07-31,76.4 3136 | 2021-08-01,74.01 3137 | 2021-08-02,76.36 3138 | 2021-08-03,89.7 3139 | 2021-08-04,89.66 3140 | 2021-08-05,73.19 3141 | 2021-08-06,70.69 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3635 | 2022-12-13,92.75 3636 | 2022-12-14,104.46 3637 | 2022-12-15,96.74 3638 | 2022-12-16,100.63 3639 | 2022-12-17,102.55 3640 | 2022-12-18,95.32 3641 | 2022-12-19,91.0 3642 | 2022-12-20,91.03 3643 | 2022-12-21,96.26 3644 | 2022-12-22,95.45 3645 | 2022-12-23,98.46 3646 | 2022-12-24,100.43 3647 | 2022-12-25,104.37 3648 | 2022-12-26,106.25 3649 | 2022-12-27,99.2 3650 | 2022-12-28,102.61 3651 | 2022-12-29,106.32 3652 | 2022-12-30,95.19 3653 | 2022-12-31,94.12 3654 | 2023-01-01,92.36 3655 | -------------------------------------------------------------------------------- /Chapter 2/Chapter_2_Data_Representation.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Chapter 2-Data Representation.ipynb", 7 | "provenance": [], 8 | "collapsed_sections": [] 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "source": [ 22 | "#TensorFlow Developer Certificate\n", 23 | "\n", 24 | "## Introduction to TensorFlow (CHAPTER 2)" 25 | ], 26 | "metadata": { 27 | "id": "VrgpYNd7FO95" 28 | } 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "source": [ 33 | " ## Data Representation \n", 34 | "\n", 35 | "Reference 1 for Tensors: https://www.tensorflow.org/api_docs/python/tf/Tensor\n", 36 | "\n", 37 | "Reference 2 for errors: https://www.tensorflow.org/api_docs/python/tf/errors\n", 38 | "\n", 39 | "Reference 3 for Hello World: https://www.tensorflow.org/tutorials/keras/regression\n", 40 | "\n", 41 | "https://www.youtube.com/watch?v=-vHQub0NXI4&list=RDCMUC0rqucBdTuFTjJiefW5t-IQ&start_radio=1\n", 42 | "\n", 43 | "The goal of this notebook is to understand data represenation with TensorFlow" 44 | ], 45 | "metadata": { 46 | "id": "-icxkgqfFX9I" 47 | } 48 | }, 49 | { 50 | "cell_type": "code", 51 | "source": [ 52 | "#pip install Tensorflow" 53 | ], 54 | "metadata": { 55 | "id": "7QSguJQEFXY5" 56 | }, 57 | "execution_count": 51, 58 | "outputs": [] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "source": [ 63 | "#Import required libraries\n", 64 | "import tensorflow as tf\n", 65 | "import numpy as np\n", 66 | "from numpy import *" 67 | ], 68 | "metadata": { 69 | "id": "LvKyHksAFwy4" 70 | }, 71 | "execution_count": 52, 72 | "outputs": [] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "source": [ 77 | "print(tf.__version__)" 78 | ], 79 | "metadata": { 80 | "colab": { 81 | "base_uri": "https://localhost:8080/" 82 | }, 83 | "id": "Oz686gfbFwwn", 84 | "outputId": "01255fdc-1481-46a0-98ff-ca89a6ae4ced" 85 | }, 86 | "execution_count": 53, 87 | "outputs": [ 88 | { 89 | "output_type": "stream", 90 | "name": "stdout", 91 | "text": [ 92 | "2.8.0\n" 93 | ] 94 | } 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "source": [ 100 | "#Creating tensors using \n", 101 | "* tf.constant()\n", 102 | "* tf.variables()\n", 103 | "* tf.range()" 104 | ], 105 | "metadata": { 106 | "id": "T18sUsLyF8rY" 107 | } 108 | }, 109 | { 110 | "cell_type": "code", 111 | "source": [ 112 | "#Creating a tensor object using tf constant\n", 113 | "a_constant = tf.constant([1, 2, 3, 4 ,5,6])\n", 114 | "a_constant" 115 | ], 116 | "metadata": { 117 | "colab": { 118 | "base_uri": "https://localhost:8080/" 119 | }, 120 | "id": "yqMgvGzRF8EA", 121 | "outputId": "285206fe-dade-4de6-cb1f-a4f537cf401f" 122 | }, 123 | "execution_count": 54, 124 | "outputs": [ 125 | { 126 | "output_type": "execute_result", 127 | "data": { 128 | "text/plain": [ 129 | "" 130 | ] 131 | }, 132 | "metadata": {}, 133 | "execution_count": 54 134 | } 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "source": [ 140 | "#Creating a tensor object using tf constant \n", 141 | "a_variable = tf.Variable([1, 2, 3, 4 ,5,6])\n", 142 | "a_variable" 143 | ], 144 | "metadata": { 145 | "colab": { 146 | "base_uri": "https://localhost:8080/" 147 | }, 148 | "id": "-t5sTejuFwrw", 149 | "outputId": "f155db5c-13c6-41f3-e888-9b8f954537ee" 150 | }, 151 | "execution_count": 55, 152 | "outputs": [ 153 | { 154 | "output_type": "execute_result", 155 | "data": { 156 | "text/plain": [ 157 | "" 158 | ] 159 | }, 160 | "metadata": {}, 161 | "execution_count": 55 162 | } 163 | ] 164 | }, 165 | { 166 | "cell_type": "code", 167 | "source": [ 168 | "# Creating tensors using the range function \n", 169 | "a_range = tf.range(start=1, limit=7)\n", 170 | "a_range" 171 | ], 172 | "metadata": { 173 | "colab": { 174 | "base_uri": "https://localhost:8080/" 175 | }, 176 | "id": "GzEsleVzFwpI", 177 | "outputId": "cc0b805b-0650-43b5-f339-360e1ab0404b" 178 | }, 179 | "execution_count": 56, 180 | "outputs": [ 181 | { 182 | "output_type": "execute_result", 183 | "data": { 184 | "text/plain": [ 185 | "" 186 | ] 187 | }, 188 | "metadata": {}, 189 | "execution_count": 56 190 | } 191 | ] 192 | }, 193 | { 194 | "cell_type": "markdown", 195 | "source": [ 196 | "#Tensor Rank" 197 | ], 198 | "metadata": { 199 | "id": "0-muuKARHHt3" 200 | } 201 | }, 202 | { 203 | "cell_type": "code", 204 | "source": [ 205 | "#scalar\n", 206 | "a = tf.constant(1)\n", 207 | "a" 208 | ], 209 | "metadata": { 210 | "colab": { 211 | "base_uri": "https://localhost:8080/" 212 | }, 213 | "id": "ZT0ldHH5HHRY", 214 | "outputId": "2f6dc778-a327-4921-cd0d-9147947efbd2" 215 | }, 216 | "execution_count": 57, 217 | "outputs": [ 218 | { 219 | "output_type": "execute_result", 220 | "data": { 221 | "text/plain": [ 222 | "" 223 | ] 224 | }, 225 | "metadata": {}, 226 | "execution_count": 57 227 | } 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": 58, 233 | "metadata": { 234 | "colab": { 235 | "base_uri": "https://localhost:8080/" 236 | }, 237 | "id": "VLP38zPeFOap", 238 | "outputId": "ab7e7b5f-f9f8-4098-e48d-3655f4f4e03f" 239 | }, 240 | "outputs": [ 241 | { 242 | "output_type": "execute_result", 243 | "data": { 244 | "text/plain": [ 245 | "" 246 | ] 247 | }, 248 | "metadata": {}, 249 | "execution_count": 58 250 | } 251 | ], 252 | "source": [ 253 | "#vector \n", 254 | "b= tf.constant([1.2,2.3,3.4,4.5])\n", 255 | "b" 256 | ] 257 | }, 258 | { 259 | "cell_type": "code", 260 | "source": [ 261 | "#matrix\n", 262 | "c =tf.constant([[1,2],[3,4]])\n", 263 | "c" 264 | ], 265 | "metadata": { 266 | "colab": { 267 | "base_uri": "https://localhost:8080/" 268 | }, 269 | "id": "uo-kFVWsHKen", 270 | "outputId": "86413e71-d967-4400-ad6e-8a97b9cba079" 271 | }, 272 | "execution_count": 59, 273 | "outputs": [ 274 | { 275 | "output_type": "execute_result", 276 | "data": { 277 | "text/plain": [ 278 | "" 281 | ] 282 | }, 283 | "metadata": {}, 284 | "execution_count": 59 285 | } 286 | ] 287 | }, 288 | { 289 | "cell_type": "code", 290 | "source": [ 291 | "#3-dimensional tensor\n", 292 | "d=tf.constant([[[1,2],[3,4],[5,6]],[[7,8],[9,10],[11,12]]])\n", 293 | "d" 294 | ], 295 | "metadata": { 296 | "colab": { 297 | "base_uri": "https://localhost:8080/" 298 | }, 299 | "id": "8x0WnEmBHKb5", 300 | "outputId": "5d7e7889-3b9b-4531-93fb-05374ad81e25" 301 | }, 302 | "execution_count": 60, 303 | "outputs": [ 304 | { 305 | "output_type": "execute_result", 306 | "data": { 307 | "text/plain": [ 308 | "" 316 | ] 317 | }, 318 | "metadata": {}, 319 | "execution_count": 60 320 | } 321 | ] 322 | }, 323 | { 324 | "cell_type": "markdown", 325 | "source": [ 326 | "# Properties of tensors" 327 | ], 328 | "metadata": { 329 | "id": "c4cbGZHTJT5P" 330 | } 331 | }, 332 | { 333 | "cell_type": "code", 334 | "source": [ 335 | "#scalar\n", 336 | "a = tf.Variable(1.1, name=\"TDC\", dtype=float16)\n", 337 | "a" 338 | ], 339 | "metadata": { 340 | "colab": { 341 | "base_uri": "https://localhost:8080/" 342 | }, 343 | "id": "AMPCQ34IHKZg", 344 | "outputId": "48650ff4-3c12-4f30-c02b-82a055ddce57" 345 | }, 346 | "execution_count": 61, 347 | "outputs": [ 348 | { 349 | "output_type": "execute_result", 350 | "data": { 351 | "text/plain": [ 352 | "" 353 | ] 354 | }, 355 | "metadata": {}, 356 | "execution_count": 61 357 | } 358 | ] 359 | }, 360 | { 361 | "cell_type": "code", 362 | "source": [ 363 | "#vector \n", 364 | "b= tf.Variable([1.2,2.3,3.4,4.5], name=\"'Vector\",dtype=float64)\n", 365 | "b" 366 | ], 367 | "metadata": { 368 | "colab": { 369 | "base_uri": "https://localhost:8080/" 370 | }, 371 | "id": "HSNKUJpiHKXI", 372 | "outputId": "4fc1382b-c651-44b1-ce62-46e7405d26bd" 373 | }, 374 | "execution_count": 62, 375 | "outputs": [ 376 | { 377 | "output_type": "execute_result", 378 | "data": { 379 | "text/plain": [ 380 | "" 381 | ] 382 | }, 383 | "metadata": {}, 384 | "execution_count": 62 385 | } 386 | ] 387 | }, 388 | { 389 | "cell_type": "markdown", 390 | "source": [ 391 | "### Rank of a tensor" 392 | ], 393 | "metadata": { 394 | "id": "Sj3gkIS0KdRv" 395 | } 396 | }, 397 | { 398 | "cell_type": "code", 399 | "source": [ 400 | "#scalar\n", 401 | "a = tf.constant(1.1)\n", 402 | "#vector\n", 403 | "b= tf.constant([1.2,2.3,3.4,4.5])\n", 404 | "#matrix\n", 405 | "c =tf.constant([[1,2],[3,4]])\n", 406 | "#Generating tensor rank\n", 407 | "print(\"The rank of the scalar is: \",tf.rank(a))\n", 408 | "print(\" \")\n", 409 | "print(\"The rank of the vector is: \",tf.rank(b))\n", 410 | "print(\" \")\n", 411 | "print(\"The rank of the matrix is: \",tf.rank(c))\n" 412 | ], 413 | "metadata": { 414 | "colab": { 415 | "base_uri": "https://localhost:8080/" 416 | }, 417 | "id": "9qF3YgeDHKUp", 418 | "outputId": "8010f0bb-da4a-40b8-8a59-f8709e42d5d4" 419 | }, 420 | "execution_count": 63, 421 | "outputs": [ 422 | { 423 | "output_type": "stream", 424 | "name": "stdout", 425 | "text": [ 426 | "The rank of the scalar is: tf.Tensor(0, shape=(), dtype=int32)\n", 427 | " \n", 428 | "The rank of the vector is: tf.Tensor(1, shape=(), dtype=int32)\n", 429 | " \n", 430 | "The rank of the matrix is: tf.Tensor(2, shape=(), dtype=int32)\n" 431 | ] 432 | } 433 | ] 434 | }, 435 | { 436 | "cell_type": "code", 437 | "source": [ 438 | "#Generating details of the dimension\n", 439 | "\n", 440 | "print(\"The dimension of the scalar is: \",a.ndim)\n", 441 | "print(\" \")\n", 442 | "print(\"The dimension of the vector is: \",b.ndim)\n", 443 | "print(\" \")\n", 444 | "print(\"The dimension of the matrix is: \",c.ndim)\n" 445 | ], 446 | "metadata": { 447 | "colab": { 448 | "base_uri": "https://localhost:8080/" 449 | }, 450 | "id": "_hrrVEZ7HKSA", 451 | "outputId": "cbc17fa2-e638-48e4-9b9c-c6024a5b81b9" 452 | }, 453 | "execution_count": 64, 454 | "outputs": [ 455 | { 456 | "output_type": "stream", 457 | "name": "stdout", 458 | "text": [ 459 | "The dimension of the scalar is: 0\n", 460 | " \n", 461 | "The dimension of the vector is: 1\n", 462 | " \n", 463 | "The dimension of the matrix is: 2\n" 464 | ] 465 | } 466 | ] 467 | }, 468 | { 469 | "cell_type": "markdown", 470 | "source": [ 471 | "### Datatype of a tensor" 472 | ], 473 | "metadata": { 474 | "id": "TXDSsYDNLthv" 475 | } 476 | }, 477 | { 478 | "cell_type": "code", 479 | "source": [ 480 | "#printing the data type \n", 481 | "print(\"The data type of the scalar is: \",a.dtype)\n", 482 | "print(\" \")\n", 483 | "print(\"The data type of the vector is: \",b.dtype)\n", 484 | "print(\" \")\n", 485 | "print(\"The data type of the matrix is: \",c.dtype)\n" 486 | ], 487 | "metadata": { 488 | "colab": { 489 | "base_uri": "https://localhost:8080/" 490 | }, 491 | "id": "xEAMQ4b2HKPY", 492 | "outputId": "4cae4b40-c8cb-4b02-aa28-4b29b8c9725e" 493 | }, 494 | "execution_count": 65, 495 | "outputs": [ 496 | { 497 | "output_type": "stream", 498 | "name": "stdout", 499 | "text": [ 500 | "The data type of the scalar is: \n", 501 | " \n", 502 | "The data type of the vector is: \n", 503 | " \n", 504 | "The data type of the matrix is: \n" 505 | ] 506 | } 507 | ] 508 | }, 509 | { 510 | "cell_type": "markdown", 511 | "source": [ 512 | "### Shape of tensors" 513 | ], 514 | "metadata": { 515 | "id": "AwrWG4O1MGHH" 516 | } 517 | }, 518 | { 519 | "cell_type": "code", 520 | "source": [ 521 | "#Generating details of the tensor shape\n", 522 | "print(\"The Shape of the scalar is: \",a.shape)\n", 523 | "print(\" \")\n", 524 | "print(\"The Shape of the vector is: \",b.shape)\n", 525 | "print(\" \")\n", 526 | "print(\"The Shape of the matrix is: \",c.shape)\n" 527 | ], 528 | "metadata": { 529 | "colab": { 530 | "base_uri": "https://localhost:8080/" 531 | }, 532 | "id": "CNRjXHxdHKMx", 533 | "outputId": "de9dc3c2-6c1f-4a7d-ed3e-757f2b17dfdd" 534 | }, 535 | "execution_count": 66, 536 | "outputs": [ 537 | { 538 | "output_type": "stream", 539 | "name": "stdout", 540 | "text": [ 541 | "The Shape of the scalar is: ()\n", 542 | " \n", 543 | "The Shape of the vector is: (4,)\n", 544 | " \n", 545 | "The Shape of the matrix is: (2, 2)\n" 546 | ] 547 | } 548 | ] 549 | }, 550 | { 551 | "cell_type": "code", 552 | "source": [ 553 | "#Generating number of elements in a tensor \n", 554 | "print(\"The Size of the scalar is: \",tf.size(a))\n", 555 | "print(\" \")\n", 556 | "print(\"The Size of the vector is: \",tf.size(b))\n", 557 | "print(\" \")\n", 558 | "print(\"The Size of the matrix is: \",tf.size(c))\n" 559 | ], 560 | "metadata": { 561 | "colab": { 562 | "base_uri": "https://localhost:8080/" 563 | }, 564 | "id": "BARmRHMiHJto", 565 | "outputId": "01ba754d-da3c-4a5e-a02f-55f0451c831f" 566 | }, 567 | "execution_count": 67, 568 | "outputs": [ 569 | { 570 | "output_type": "stream", 571 | "name": "stdout", 572 | "text": [ 573 | "The Size of the scalar is: tf.Tensor(1, shape=(), dtype=int32)\n", 574 | " \n", 575 | "The Size of the vector is: tf.Tensor(4, shape=(), dtype=int32)\n", 576 | " \n", 577 | "The Size of the matrix is: tf.Tensor(4, shape=(), dtype=int32)\n" 578 | ] 579 | } 580 | ] 581 | }, 582 | { 583 | "cell_type": "markdown", 584 | "source": [ 585 | "#Basic tensor operations" 586 | ], 587 | "metadata": { 588 | "id": "oEQPrseRM8lB" 589 | } 590 | }, 591 | { 592 | "cell_type": "markdown", 593 | "source": [ 594 | "### Changing Data type" 595 | ], 596 | "metadata": { 597 | "id": "gD9Q5xGLNFaQ" 598 | } 599 | }, 600 | { 601 | "cell_type": "code", 602 | "source": [ 603 | "a=tf.constant([1,2,3,4,5])\n", 604 | "a" 605 | ], 606 | "metadata": { 607 | "colab": { 608 | "base_uri": "https://localhost:8080/" 609 | }, 610 | "id": "paNxk66vHJrS", 611 | "outputId": "5bc9ff91-cd71-435c-e1fa-127a7cdb5f00" 612 | }, 613 | "execution_count": 68, 614 | "outputs": [ 615 | { 616 | "output_type": "execute_result", 617 | "data": { 618 | "text/plain": [ 619 | "" 620 | ] 621 | }, 622 | "metadata": {}, 623 | "execution_count": 68 624 | } 625 | ] 626 | }, 627 | { 628 | "cell_type": "code", 629 | "source": [ 630 | "#changing the datatype from int32 to float 32\n", 631 | "a =tf.cast(a,dtype=float32)\n", 632 | "a" 633 | ], 634 | "metadata": { 635 | "colab": { 636 | "base_uri": "https://localhost:8080/" 637 | }, 638 | "id": "-bKMr5OSHJpA", 639 | "outputId": "095cb7e5-7142-486f-c41c-b3eff834229d" 640 | }, 641 | "execution_count": 69, 642 | "outputs": [ 643 | { 644 | "output_type": "execute_result", 645 | "data": { 646 | "text/plain": [ 647 | "" 648 | ] 649 | }, 650 | "metadata": {}, 651 | "execution_count": 69 652 | } 653 | ] 654 | }, 655 | { 656 | "cell_type": "markdown", 657 | "source": [ 658 | "### Indexing " 659 | ], 660 | "metadata": { 661 | "id": "SLIhQ45zNqx_" 662 | } 663 | }, 664 | { 665 | "cell_type": "code", 666 | "source": [ 667 | "# Create a 2 x2 matrix \n", 668 | "a = tf.constant([[1, 2],[3, 4]], dtype=float32)\n", 669 | "a" 670 | ], 671 | "metadata": { 672 | "colab": { 673 | "base_uri": "https://localhost:8080/" 674 | }, 675 | "id": "D4Hrd0dgHJmg", 676 | "outputId": "3a887dfa-6428-4422-b716-3edbbf81d038" 677 | }, 678 | "execution_count": 70, 679 | "outputs": [ 680 | { 681 | "output_type": "execute_result", 682 | "data": { 683 | "text/plain": [ 684 | "" 687 | ] 688 | }, 689 | "metadata": {}, 690 | "execution_count": 70 691 | } 692 | ] 693 | }, 694 | { 695 | "cell_type": "code", 696 | "source": [ 697 | "# Indexing using method 1\n", 698 | "a[0]" 699 | ], 700 | "metadata": { 701 | "colab": { 702 | "base_uri": "https://localhost:8080/" 703 | }, 704 | "id": "pZZOT4q6HJkJ", 705 | "outputId": "50190513-ee50-4cc3-f0d6-b7c2ce4a1846" 706 | }, 707 | "execution_count": 71, 708 | "outputs": [ 709 | { 710 | "output_type": "execute_result", 711 | "data": { 712 | "text/plain": [ 713 | "" 714 | ] 715 | }, 716 | "metadata": {}, 717 | "execution_count": 71 718 | } 719 | ] 720 | }, 721 | { 722 | "cell_type": "code", 723 | "source": [ 724 | "# Indexing \n", 725 | "a[0][1]" 726 | ], 727 | "metadata": { 728 | "colab": { 729 | "base_uri": "https://localhost:8080/" 730 | }, 731 | "id": "fRie0diIHJhg", 732 | "outputId": "2b38568e-7ee8-4ce5-bc5b-36e5bcb68a8d" 733 | }, 734 | "execution_count": 72, 735 | "outputs": [ 736 | { 737 | "output_type": "execute_result", 738 | "data": { 739 | "text/plain": [ 740 | "" 741 | ] 742 | }, 743 | "metadata": {}, 744 | "execution_count": 72 745 | } 746 | ] 747 | }, 748 | { 749 | "cell_type": "code", 750 | "source": [ 751 | "#Method 2\n", 752 | "c = tf.constant([0, 1, 2, 3, 4, 5])\n", 753 | "print(tf.slice(c,begin=[2],size=[4]))\n" 754 | ], 755 | "metadata": { 756 | "colab": { 757 | "base_uri": "https://localhost:8080/" 758 | }, 759 | "id": "ZxcpnPYBHJeo", 760 | "outputId": "1f52b78a-4b4c-4e83-8aeb-7188d5b4a0ef" 761 | }, 762 | "execution_count": 73, 763 | "outputs": [ 764 | { 765 | "output_type": "stream", 766 | "name": "stdout", 767 | "text": [ 768 | "tf.Tensor([2 3 4 5], shape=(4,), dtype=int32)\n" 769 | ] 770 | } 771 | ] 772 | }, 773 | { 774 | "cell_type": "markdown", 775 | "source": [ 776 | "### Expanding a matrix" 777 | ], 778 | "metadata": { 779 | "id": "yTM2g9PIQMxG" 780 | } 781 | }, 782 | { 783 | "cell_type": "code", 784 | "source": [ 785 | "tf.expand_dims(a,axis=0)" 786 | ], 787 | "metadata": { 788 | "colab": { 789 | "base_uri": "https://localhost:8080/" 790 | }, 791 | "id": "0j8NnLq-Nx4c", 792 | "outputId": "338a1222-a21c-4cb9-889d-11cd20799b73" 793 | }, 794 | "execution_count": 74, 795 | "outputs": [ 796 | { 797 | "output_type": "execute_result", 798 | "data": { 799 | "text/plain": [ 800 | "" 803 | ] 804 | }, 805 | "metadata": {}, 806 | "execution_count": 74 807 | } 808 | ] 809 | }, 810 | { 811 | "cell_type": "code", 812 | "source": [ 813 | "(tf.expand_dims(a,axis=0)).shape,(tf.expand_dims(a,axis=1)).shape,(tf.expand_dims(a,axis=-1)).shape" 814 | ], 815 | "metadata": { 816 | "colab": { 817 | "base_uri": "https://localhost:8080/" 818 | }, 819 | "id": "wObI_ThDNx2f", 820 | "outputId": "f1779ff6-df57-4149-a338-f8a64188f6ae" 821 | }, 822 | "execution_count": 75, 823 | "outputs": [ 824 | { 825 | "output_type": "execute_result", 826 | "data": { 827 | "text/plain": [ 828 | "(TensorShape([1, 2, 2]), TensorShape([2, 1, 2]), TensorShape([2, 2, 1]))" 829 | ] 830 | }, 831 | "metadata": {}, 832 | "execution_count": 75 833 | } 834 | ] 835 | }, 836 | { 837 | "cell_type": "markdown", 838 | "source": [ 839 | "### Tensor Aggregation" 840 | ], 841 | "metadata": { 842 | "id": "wege9xaGRSLr" 843 | } 844 | }, 845 | { 846 | "cell_type": "code", 847 | "source": [ 848 | "import random\n", 849 | "a = random.sample(range(1, 100), 50)\n", 850 | "a = tf.constant(a)" 851 | ], 852 | "metadata": { 853 | "id": "xu9cHsi3Nx0Z" 854 | }, 855 | "execution_count": 76, 856 | "outputs": [] 857 | }, 858 | { 859 | "cell_type": "code", 860 | "source": [ 861 | "a" 862 | ], 863 | "metadata": { 864 | "colab": { 865 | "base_uri": "https://localhost:8080/" 866 | }, 867 | "id": "JoM_c9loSjD2", 868 | "outputId": "1b2671cf-182a-4df0-959e-7b9476a319bc" 869 | }, 870 | "execution_count": 77, 871 | "outputs": [ 872 | { 873 | "output_type": "execute_result", 874 | "data": { 875 | "text/plain": [ 876 | "" 881 | ] 882 | }, 883 | "metadata": {}, 884 | "execution_count": 77 885 | } 886 | ] 887 | }, 888 | { 889 | "cell_type": "code", 890 | "source": [ 891 | "print(\"The smallest number in our vector is : \",tf.reduce_min(a))\n", 892 | "print(\" \")\n", 893 | "print(\"The largest number in our vector is: \",tf.reduce_max(a))\n", 894 | "print(\" \")\n", 895 | "print(\"The sum of our vector is : \",tf.reduce_sum(a))\n", 896 | "print(\" \")\n", 897 | "print(\"The mean of our vector is: \",tf.reduce_mean(a))" 898 | ], 899 | "metadata": { 900 | "colab": { 901 | "base_uri": "https://localhost:8080/" 902 | }, 903 | "id": "OE83loT-NxyL", 904 | "outputId": "04d25d86-b645-4166-fe58-85127ea0d976" 905 | }, 906 | "execution_count": 78, 907 | "outputs": [ 908 | { 909 | "output_type": "stream", 910 | "name": "stdout", 911 | "text": [ 912 | "The smallest number in our vector is : tf.Tensor(2, shape=(), dtype=int32)\n", 913 | " \n", 914 | "The largest number in our vector is: tf.Tensor(97, shape=(), dtype=int32)\n", 915 | " \n", 916 | "The sum of our vector is : tf.Tensor(2641, shape=(), dtype=int32)\n", 917 | " \n", 918 | "The mean of our vector is: tf.Tensor(52, shape=(), dtype=int32)\n" 919 | ] 920 | } 921 | ] 922 | }, 923 | { 924 | "cell_type": "code", 925 | "source": [ 926 | "print(\"The position that holds the lowest value is : \",tf.argmin(a))\n", 927 | "print(\" \")\n", 928 | "print(\"The position that holds the highest value is: \",tf.argmax(a))" 929 | ], 930 | "metadata": { 931 | "colab": { 932 | "base_uri": "https://localhost:8080/" 933 | }, 934 | "id": "NqLDhBYcNxvv", 935 | "outputId": "76324171-0cfe-4bba-9ba2-65b3934680af" 936 | }, 937 | "execution_count": 79, 938 | "outputs": [ 939 | { 940 | "output_type": "stream", 941 | "name": "stdout", 942 | "text": [ 943 | "The position that holds the lowest value is : tf.Tensor(18, shape=(), dtype=int64)\n", 944 | " \n", 945 | "The position that holds the highest value is: tf.Tensor(17, shape=(), dtype=int64)\n" 946 | ] 947 | } 948 | ] 949 | }, 950 | { 951 | "cell_type": "code", 952 | "source": [ 953 | "a[tf.argmin(a)], a[tf.argmax(a)]" 954 | ], 955 | "metadata": { 956 | "colab": { 957 | "base_uri": "https://localhost:8080/" 958 | }, 959 | "id": "Yd-xKsN3Nxty", 960 | "outputId": "89136433-1641-46a0-979f-3ac44609d30e" 961 | }, 962 | "execution_count": 80, 963 | "outputs": [ 964 | { 965 | "output_type": "execute_result", 966 | "data": { 967 | "text/plain": [ 968 | "(,\n", 969 | " )" 970 | ] 971 | }, 972 | "metadata": {}, 973 | "execution_count": 80 974 | } 975 | ] 976 | }, 977 | { 978 | "cell_type": "markdown", 979 | "source": [ 980 | "### Transpose and Reshape " 981 | ], 982 | "metadata": { 983 | "id": "2Nl3iD7BUiE4" 984 | } 985 | }, 986 | { 987 | "cell_type": "code", 988 | "source": [ 989 | "# Create a 3 x 4 matrix \n", 990 | "a = tf.constant([[1, 2,3,4],[5,6,7,8], [9,10,11,12]])\n", 991 | "a" 992 | ], 993 | "metadata": { 994 | "colab": { 995 | "base_uri": "https://localhost:8080/" 996 | }, 997 | "id": "MrKOQziQNxre", 998 | "outputId": "ea0d8990-1f9d-47b6-9a1f-c2c05b22981e" 999 | }, 1000 | "execution_count": 81, 1001 | "outputs": [ 1002 | { 1003 | "output_type": "execute_result", 1004 | "data": { 1005 | "text/plain": [ 1006 | "" 1010 | ] 1011 | }, 1012 | "metadata": {}, 1013 | "execution_count": 81 1014 | } 1015 | ] 1016 | }, 1017 | { 1018 | "cell_type": "code", 1019 | "source": [ 1020 | "tf.reshape(a, shape=(2, 2,3))" 1021 | ], 1022 | "metadata": { 1023 | "colab": { 1024 | "base_uri": "https://localhost:8080/" 1025 | }, 1026 | "id": "D-CNMr6kNxpS", 1027 | "outputId": "a9a0aaa8-8d4e-4e6c-c5d8-9f2db80162a0" 1028 | }, 1029 | "execution_count": 82, 1030 | "outputs": [ 1031 | { 1032 | "output_type": "execute_result", 1033 | "data": { 1034 | "text/plain": [ 1035 | "" 1041 | ] 1042 | }, 1043 | "metadata": {}, 1044 | "execution_count": 82 1045 | } 1046 | ] 1047 | }, 1048 | { 1049 | "cell_type": "code", 1050 | "source": [ 1051 | "tf.transpose(a)" 1052 | ], 1053 | "metadata": { 1054 | "colab": { 1055 | "base_uri": "https://localhost:8080/" 1056 | }, 1057 | "id": "wqmAfeEhNxnH", 1058 | "outputId": "1f67cabc-6c9f-4856-9038-81f9a3b716bc" 1059 | }, 1060 | "execution_count": 83, 1061 | "outputs": [ 1062 | { 1063 | "output_type": "execute_result", 1064 | "data": { 1065 | "text/plain": [ 1066 | "" 1071 | ] 1072 | }, 1073 | "metadata": {}, 1074 | "execution_count": 83 1075 | } 1076 | ] 1077 | }, 1078 | { 1079 | "cell_type": "markdown", 1080 | "source": [ 1081 | "### Element wise operations " 1082 | ], 1083 | "metadata": { 1084 | "id": "PFoJEu5KWRxQ" 1085 | } 1086 | }, 1087 | { 1088 | "cell_type": "code", 1089 | "source": [ 1090 | "a= tf.constant([1,2,3])\n", 1091 | "a" 1092 | ], 1093 | "metadata": { 1094 | "colab": { 1095 | "base_uri": "https://localhost:8080/" 1096 | }, 1097 | "id": "91GSQL5oNxko", 1098 | "outputId": "fa208589-aa7b-4cf7-a42e-182672f7cd61" 1099 | }, 1100 | "execution_count": 84, 1101 | "outputs": [ 1102 | { 1103 | "output_type": "execute_result", 1104 | "data": { 1105 | "text/plain": [ 1106 | "" 1107 | ] 1108 | }, 1109 | "metadata": {}, 1110 | "execution_count": 84 1111 | } 1112 | ] 1113 | }, 1114 | { 1115 | "cell_type": "code", 1116 | "source": [ 1117 | "#Addition operation\n", 1118 | "print(a+4)\n", 1119 | "print(\" \")\n", 1120 | "#Subtraction operation\n", 1121 | "print(a-4)\n", 1122 | "print(\" \")\n", 1123 | "#Multiplication Operation\n", 1124 | "print(a*4)\n", 1125 | "print(\" \")\n", 1126 | "#Division Operation\n", 1127 | "print(a/4)\n", 1128 | "print(\" \")" 1129 | ], 1130 | "metadata": { 1131 | "colab": { 1132 | "base_uri": "https://localhost:8080/" 1133 | }, 1134 | "id": "KrgMJZ0RNxhn", 1135 | "outputId": "8c73efd1-1733-4dbb-a85a-888bcc608af5" 1136 | }, 1137 | "execution_count": 85, 1138 | "outputs": [ 1139 | { 1140 | "output_type": "stream", 1141 | "name": "stdout", 1142 | "text": [ 1143 | "tf.Tensor([5 6 7], shape=(3,), dtype=int32)\n", 1144 | " \n", 1145 | "tf.Tensor([-3 -2 -1], shape=(3,), dtype=int32)\n", 1146 | " \n", 1147 | "tf.Tensor([ 4 8 12], shape=(3,), dtype=int32)\n", 1148 | " \n", 1149 | "tf.Tensor([0.25 0.5 0.75], shape=(3,), dtype=float64)\n", 1150 | " \n" 1151 | ] 1152 | } 1153 | ] 1154 | }, 1155 | { 1156 | "cell_type": "markdown", 1157 | "source": [ 1158 | "### Matrix multiplication" 1159 | ], 1160 | "metadata": { 1161 | "id": "sAw4iecFXdUX" 1162 | } 1163 | }, 1164 | { 1165 | "cell_type": "code", 1166 | "source": [ 1167 | "# 3 X 2 MATRIX\n", 1168 | "a = tf.constant([[1, 2],\n", 1169 | " [3, 4],\n", 1170 | " [5, 6]])\n", 1171 | "\n", 1172 | "#2 X 3 MATRIX\n", 1173 | "b = tf.constant([[7,8,9],\n", 1174 | " [10,11,12]])" 1175 | ], 1176 | "metadata": { 1177 | "id": "eOEia1O2Wkth" 1178 | }, 1179 | "execution_count": 86, 1180 | "outputs": [] 1181 | }, 1182 | { 1183 | "cell_type": "code", 1184 | "source": [ 1185 | "#matrix muplitication\n", 1186 | "tf.matmul(a,b)" 1187 | ], 1188 | "metadata": { 1189 | "colab": { 1190 | "base_uri": "https://localhost:8080/" 1191 | }, 1192 | "id": "C9lCt03mWkq_", 1193 | "outputId": "d97af980-3f54-4bf5-ab57-61d7809e6439" 1194 | }, 1195 | "execution_count": 87, 1196 | "outputs": [ 1197 | { 1198 | "output_type": "execute_result", 1199 | "data": { 1200 | "text/plain": [ 1201 | "" 1205 | ] 1206 | }, 1207 | "metadata": {}, 1208 | "execution_count": 87 1209 | } 1210 | ] 1211 | }, 1212 | { 1213 | "cell_type": "code", 1214 | "source": [ 1215 | "#Mutiplying a with its transpose\n", 1216 | "tf.matmul(a,tf.transpose(a))" 1217 | ], 1218 | "metadata": { 1219 | "colab": { 1220 | "base_uri": "https://localhost:8080/" 1221 | }, 1222 | "id": "munnxN7qWko4", 1223 | "outputId": "73c4aace-950a-4004-bc96-17d8e3423fc9" 1224 | }, 1225 | "execution_count": 88, 1226 | "outputs": [ 1227 | { 1228 | "output_type": "execute_result", 1229 | "data": { 1230 | "text/plain": [ 1231 | "" 1235 | ] 1236 | }, 1237 | "metadata": {}, 1238 | "execution_count": 88 1239 | } 1240 | ] 1241 | }, 1242 | { 1243 | "cell_type": "code", 1244 | "source": [ 1245 | "#Mutiplying a with reshaped a. Hope you tried it out yourself?\n", 1246 | "tf.matmul(a,tf.reshape(a, shape =(2,3)))" 1247 | ], 1248 | "metadata": { 1249 | "colab": { 1250 | "base_uri": "https://localhost:8080/" 1251 | }, 1252 | "id": "9Ae68LtiWkma", 1253 | "outputId": "4e5b5f14-249e-42a5-ff4f-1d18f98818db" 1254 | }, 1255 | "execution_count": 89, 1256 | "outputs": [ 1257 | { 1258 | "output_type": "execute_result", 1259 | "data": { 1260 | "text/plain": [ 1261 | "" 1265 | ] 1266 | }, 1267 | "metadata": {}, 1268 | "execution_count": 89 1269 | } 1270 | ] 1271 | } 1272 | ] 1273 | } -------------------------------------------------------------------------------- /Chapter 3/new_hires.csv: -------------------------------------------------------------------------------- 1 | Name,Phone_Number,Experience,Qualification,University,Role,Cert,Date_Of_Birth 2 | Alvaro Johnson,320-636-8883,7,Bsc,Tier1,Senior,No,12/03/1978 3 | Austin Powers,903-121-1691,2,Msc,Tier1,Mid,Yes,13/03/1992 4 | Joshua Phil,673-972-2453,3,Bsc,Tier3,Mid,Yes,19/02/1988 5 | Mirinda Collins,310-364-6925,5,Msc,Tier2,Senior,No,20/03/1989 6 | Mustapha Green,401-249-3912,3,PhD,Tier1,Junior,Yes,21/03/1979 7 | Nick Freeman,875-546-2104,6,Bsc,Tier3,Junior,Yes,22/03/1982 8 | Pamela Allison,408-955-5085,2,PhD,Tier2,Junior,No,23/03/1968 9 | -------------------------------------------------------------------------------- /Chapter 3/salary_dataset.csv: -------------------------------------------------------------------------------- 1 | Name,Phone_Number,Experience,Qualification,University,Role,Cert,Date_Of_Birth,Salary 2 | Jennifer Hernandez,120-602-1220,3,Msc,Tier2,Mid,Yes,25/08/1972,98000 3 | Timothy Walker,840-675-8650,5,PhD,Tier2,Senior,Yes,03/12/2013,135500 4 | David Duran,556-293-8643,5,Msc,Tier2,Senior,Yes,19/07/2002,123500 5 | Gloria Ortega,463-559-7474,3,Bsc,Tier3,Mid,No,19/02/1970,85000 6 | Matthew Steele,968-091-7683,5,Bsc,Tier2,Senior,Yes,20/02/1970,111500 7 | Robert Hines,847-238-3139,5,PhD,Tier1,Senior,Yes,21/02/1970,131500 8 | Alex Huerta,771-319-7404,5,PhD,Tier1,Senior,Yes,11/07/1995,131500 9 | Samantha Odom,253-486-8068,5,Bsc,Tier2,Senior,Yes,25/08/1972,111500 10 | Scott Scott,655-474-9471,3,Bsc,Tier1,Junior,Yes,15/04/1988,64500 11 | Matthew Campbell,206-873-0491,5,Msc,Tier1,Senior,No,03/03/1974,112500 12 | Eric Taylor,694-480-1042,2,PhD,Tier3,Mid,Yes,19/03/1999,112000 13 | Brendan Bailey,730-684-4119,5,Bsc,Tier1,Senior,Yes,24/08/1976,107500 14 | Kyle Smith,488-416-8883,4,Bsc,Tier2,Senior,Yes,06/12/1974,107500 15 | Sarah Green,488-230-1713,1,PhD,Tier2,Mid,No,01/10/1999,95000 16 | David Aguirre,705-596-2497,4,Msc,Tier1,Senior,No,05/03/1999,108500 17 | Megan Finley,395-779-1958,4,Msc,Tier2,Senior,No,25/04/1981,112500 18 | Kelly Bell,311-532-3875,5,PhD,Tier2,Senior,Yes,26/04/1981,135500 19 | Kayla Walker,238-794-8312,5,Bsc,Tier3,Senior,Yes,27/04/1981,117500 20 | Danny Nguyen,558-902-7909,1,PhD,Tier2,Mid,Yes,14/01/1980,102000 21 | Natalie Davis,334-458-4380,5,Bsc,Tier3,Senior,Yes,10/05/2004,117500 22 | Kara Krueger,254-024-1432,5,Msc,Tier1,Senior,No,22/12/1996,112500 23 | Aaron Morgan,947-282-8926,4,PhD,Tier1,Senior,No,23/12/1996,120500 24 | James Valencia,832-374-5268,1,Msc,Tier1,Junior,No,29/10/1993,61500 25 | Andrea Scott,469-303-0864,1,Msc,Tier1,Junior,No,04/12/1997,61500 26 | Justin Hill,883-647-4802,2,Bsc,Tier2,Junior,Yes,19/08/1985,64500 27 | Robert Burke,776-279-6797,5,Bsc,Tier1,Senior,No,21/02/1975,100500 28 | Mr. Luis George,418-327-6776,5,,Tier2,Senior,Yes,15/06/2001,111500 29 | Stephanie Horn,420-783-5634,5,Bsc,Tier3,Senior,No,20/09/1999,110500 30 | Whitney Williams,947-139-1714,5,Bsc,Tier2,Senior,No,11/08/1992,104500 31 | John Curry,728-361-0721,5,Bsc,Tier2,Senior,No,30/11/1996,104500 32 | Christopher Petersen,310-123-2873,5,Msc,Tier2,Senior,Yes,16/01/1973,123500 33 | Laura Marshall,614-045-9066,5,Msc,Tier2,Senior,Yes,17/01/1973,123500 34 | William Allen,505-962-3472,1,Msc,Tier3,,Yes,18/01/1973,78500 35 | Jennifer Faulkner,824-348-4354,4,PhD,Tier3,Senior,No,19/01/1973,130500 36 | Maria Stevenson,606-604-9684,3,PhD,Tier3,Junior,Yes,26/07/1982,98500 37 | Jacqueline Jackson,717-212-8617,3,PhD,Tier3,Mid,No,05/08/1987,109000 38 | Marissa Brooks,125-100-9401,5,Bsc,Tier2,Senior,Yes,06/08/1987,111500 39 | Breanna Williams,109-762-7310,2,Bsc,Tier3,Mid,No,07/08/1987,81000 40 | William Huang,311-465-4276,5,PhD,Tier1,Senior,No,08/08/1987,124500 41 | Nicholas Rhodes,210-911-2283,5,PhD,Tier3,Senior,No,09/08/1987,134500 42 | Robert Jones,294-128-9328,2,Bsc,Tier3,Junior,Yes,10/08/1987,70500 43 | Anna Collins,966-387-9490,2,Msc,Tier1,Mid,No,17/10/1999,83000 44 | Matthew Welch,415-921-1345,5,PhD,Tier2,Senior,No,18/10/1999,128500 45 | Whitney Moore,294-513-7323,5,Msc,Tier1,,No,19/10/1999,112500 46 | Samantha Mccormick,163-004-2986,5,PhD,Tier2,Senior,No,02/04/1992,128500 47 | Stacy King,158-934-9495,5,Bsc,Tier1,Senior,No,01/05/1994,100500 48 | Mary Newman,777-929-0416,4,Bsc,Tier2,Senior,No,08/12/1972,100500 49 | Ann Rivas,912-118-8475,5,Msc,Tier2,Senior,Yes,09/12/1972,123500 50 | Robert Wong,468-904-8098,5,Msc,Tier3,,Yes,10/12/1972,129500 51 | Christopher Smith,572-391-6415,2,Bsc,Tier1,Mid,Yes,11/12/1972,78000 52 | Kayla Davis,881-554-6955,3,Msc,Tier2,Junior,No,05/12/1999,73500 53 | Laura Simpson,376-841-0833,5,Msc,Tier2,Senior,Yes,17/04/1973,123500 54 | Dustin Allen,420-194-0511,1,PhD,Tier1,Junior,No,18/04/1973,73500 55 | Kristopher Taylor,449-425-0672,5,Msc,Tier2,Senior,Yes,19/04/1973,123500 56 | Amy Jackson,235-288-6825,2,Msc,Tier2,Mid,Yes,20/04/1973,94000 57 | Jonathan Douglas,656-711-1860,5,PhD,Tier2,Senior,No,23/12/1970,128500 58 | Melanie Smith,160-299-3920,5,Bsc,Tier1,Senior,,11/10/1971,100500 59 | Daniel Patel,416-819-6256,3,Bsc,Tier2,Junior,Yes,11/03/1978,68500 60 | Tammy Smith,320-636-8883,5,Bsc,Tier1,Senior,No,12/03/1978,100500 61 | Melinda Martin,903-121-1691,2,Msc,Tier1,Mid,Yes,13/03/1978,90000 62 | Dr. Joshua Evans,673-972-2453,3,Bsc,Tier3,Mid,Yes,19/02/1988,92000 63 | Russell Medina,310-364-6925,5,Msc,Tier2,Senior,No,20/03/1989,116500 64 | Stephen Colon,401-249-3912,3,PhD,Tier1,Junior,,21/03/1989,81500 65 | Nichole Herrera,875-546-2104,2,Bsc,Tier3,Junior,Yes,22/03/1989,70500 66 | Pamela Baker,408-955-5085,2,PhD,Tier2,Junior,No,23/03/1989,81500 67 | Daniel Mays,463-644-2586,5,Msc,Tier2,Senior,Yes,24/03/1989,123500 68 | Alexa Allen,834-471-9255,4,Bsc,Tier3,Senior,Yes,04/04/1974,113500 69 | Wendy Garcia,801-100-2268,5,Bsc,Tier1,Senior,Yes,05/04/1974,107500 70 | Donna Johnson,265-390-8083,3,Bsc,Tier1,Mid,No,06/04/1974,75000 71 | Jerry Villa,490-779-7213,2,PhD,Tier1,Mid,No,07/04/1974,95000 72 | Allen Frank PhD,259-465-8631,2,Bsc,Tier2,Junior,No,29/05/1970,57500 73 | Catherine Hunter,850-935-7118,1,Bsc,Tier1,Junior,Yes,11/04/1990,56500 74 | Donald Perkins,708-475-1763,,PhD,Tier2,Senior,No,12/04/1990,128500 75 | Julie Harrington,449-564-6990,5,Msc,Tier1,Senior,Yes,30/06/1972,119500 76 | Brent Alvarez,843-373-0604,5,Bsc,Tier1,Senior,Yes,01/05/1982,107500 77 | Julie Howell,319-020-9340,1,Bsc,Tier2,Junior,No,05/12/1979,53500 78 | Fernando Bryan,751-806-7172,,Msc,Tier1,Junior,Yes,08/02/1988,68500 79 | Joseph Farmer,577-309-3413,1,Bsc,Tier3,Junior,Yes,10/07/1987,66500 80 | Joseph Moore,211-212-4864,3,Bsc,Tier1,Mid,Yes,11/07/1987,82000 81 | Laurie Murray,281-972-3370,1,Bsc,Tier3,Junior,No,12/07/1987,59500 82 | Christina Horne,294-492-0454,1,Bsc,Tier3,Junior,Yes,13/07/1987,66500 83 | Kathy Anderson,991-975-1151,3,Bsc,Tier2,Mid,No,14/07/1987,79000 84 | Nicole Griffin,945-212-9340,4,Msc,Tier1,Senior,Yes,19/08/1972,115500 85 | Terry Sanchez,290-675-8032,3,PhD,Tier1,Mid,Yes,20/08/1972,106000 86 | Jacob Curtis,814-153-9555,1,PhD,Tier2,Mid,Yes,22/12/2021,102000 87 | Colton Nelson,559-685-8692,1,Msc,Tier2,Junior,No,22/11/2020,65500 88 | Juan Brown,734-334-9685,3,Bsc,Tier3,Mid,No,14/07/1986,85000 89 | Christopher Alexander,321-025-8802,4,Bsc,Tier2,Senior,Yes,22/01/2006,107500 90 | Alicia Porter,184-963-3202,4,Bsc,Tier2,Senior,Yes,16/10/1986,107500 91 | Debra Contreras,341-195-9456,4,PhD,Tier2,Senior,Yes,20/08/2021,131500 92 | Audrey White,116-947-4040,2,PhD,Tier3,Junior,No,24/04/1989,87500 93 | Justin Tapia,632-902-8797,3,Bsc,Tier2,Mid,No,18/06/1996,79000 94 | Dustin Gonzalez,466-796-3464,5,Msc,Tier2,Senior,No,07/08/1986,116500 95 | Vincent Dickson,512-319-9324,1,Msc,Tier1,Junior,Yes,10/08/1995,68500 96 | Richard Ewing,728-719-3020,3,Msc,Tier1,Mid,No,14/01/1998,87000 97 | Courtney Bean,297-387-3910,4,Bsc,Tier3,Senior,Yes,21/04/1979,113500 98 | Kimberly Holt,688-660-2010,2,Msc,Tier1,Junior,No,22/03/2002,65500 99 | Shawn Atkinson,521-063-7189,5,Bsc,Tier1,Senior,Yes,21/03/1994,107500 100 | Katherine Miller,627-926-7659,5,Bsc,Tier3,Senior,No,23/04/1985,110500 101 | Kelly Martin,729-230-7132,3,Msc,Tier1,Junior,No,30/06/2011,69500 102 | Trevor Simpson,914-940-1661,5,Msc,Tier3,Senior,Yes,25/01/1991,129500 103 | Kevin Smith,757-431-3516,5,PhD,Tier2,Senior,Yes,26/01/1991,135500 104 | Samantha Giles PhD,984-139-5243,5,Bsc,Tier1,Senior,Yes,27/01/1991,107500 105 | Juan Cox,403-446-5514,1,Bsc,Tier1,Junior,Yes,02/05/1974,56500 106 | Christopher Olson,685-767-6795,3,Msc,Tier1,Mid,Yes,02/04/2002,94000 107 | Madeline Holmes,187-001-2664,5,Msc,Tier1,Senior,No,11/12/1986,112500 108 | Thomas Ellis,944-695-1314,1,Msc,Tier2,Junior,No,21/11/1978,65500 109 | Lauren Young,739-309-4821,5,PhD,Tier3,Senior,Yes,28/03/1971,141500 110 | Timothy Cisneros,151-404-8701,3,Bsc,Tier2,Mid,Yes,05/08/1977,86000 111 | Anita Anderson,615-620-8027,2,Bsc,Tier3,Junior,No,06/08/1977,63500 112 | Heather Miller,881-912-9126,1,Bsc,Tier3,Junior,No,21/12/2000,59500 113 | Nicholas Stafford,505-217-1313,2,Bsc,Tier2,Mid,No,13/12/1979,75000 114 | Monica Murphy,270-910-7493,4,PhD,Tier2,Senior,Yes,05/06/1992,131500 115 | Mr. Seth Terry,834-676-2346,4,Bsc,Tier3,Senior,No,08/06/1997,106500 116 | John Kramer,651-636-6807,2,Bsc,Tier2,Junior,No,08/10/2002,57500 117 | Jeanette Ferguson,249-418-4183,5,Bsc,Tier3,Senior,No,02/02/1990,110500 118 | Paula Smith,703-864-0805,1,PhD,Tier1,Junior,Yes,03/02/1990,80500 119 | Michael Archer,677-341-8713,5,Msc,Tier2,Senior,Yes,08/03/1998,123500 120 | Adam Franco,806-451-3840,3,PhD,Tier1,Mid,Yes,28/10/1972,106000 121 | Maurice Cruz,602-421-1533,5,Bsc,Tier3,Senior,Yes,29/10/1972,117500 122 | Erik Meyers,127-510-4942,1,Msc,Tier2,Junior,Yes,15/08/2003,72500 123 | Kevin Thomas,648-067-6183,2,Bsc,Tier3,Junior,No,15/05/1979,63500 124 | Ana Stanley,931-516-1305,4,Msc,Tier1,Senior,No,13/04/2003,108500 125 | Christopher Parker,336-016-9055,5,Msc,Tier1,Senior,No,19/03/1991,112500 126 | Jason Doyle,821-492-1511,2,Msc,Tier2,Mid,No,12/12/1981,87000 127 | Kristen King,225-079-5000,4,Bsc,Tier3,Senior,No,15/12/1999,106500 128 | Sean Gilbert,896-683-6187,4,Bsc,Tier3,Senior,Yes,16/12/1999,113500 129 | Richard Cortez,757-693-7853,4,Bsc,Tier1,Senior,Yes,01/11/1983,103500 130 | Shane Hayes,897-917-9551,3,Bsc,Tier3,Junior,Yes,14/01/1972,74500 131 | Kathleen Gutierrez,880-500-3179,5,Msc,Tier2,Senior,Yes,07/07/1986,123500 132 | Helen Flores,789-019-2142,3,Bsc,Tier3,Junior,Yes,05/12/1999,74500 133 | Lindsey Bauer,447-172-8422,2,Msc,Tier3,Mid,Yes,26/08/1972,100000 134 | Dr. Eric Montoya,552-846-2710,5,PhD,Tier2,Senior,No,14/11/2014,128500 135 | Mason Jacobson,321-900-1808,4,Msc,Tier1,Senior,Yes,22/09/1973,115500 136 | Joshua Harper,395-664-7246,4,Bsc,Tier3,Senior,Yes,23/09/1973,113500 137 | Michelle Kirby,884-374-4089,5,PhD,Tier1,Senior,Yes,24/09/1973,131500 138 | Anna Ford,864-013-9556,1,PhD,Tier3,Mid,No,25/09/1973,101000 139 | Claudia Mcguire,477-138-1223,5,Bsc,Tier1,Senior,Yes,26/09/1973,107500 140 | Drew Solis,388-552-5735,2,Bsc,Tier1,Mid,No,27/09/1973,71000 141 | Michael Hayes,560-075-4015,5,Msc,Tier1,Senior,Yes,28/09/1973,119500 142 | Pamela Mcdowell,611-851-9023,4,Bsc,Tier1,Mid,No,13/01/1995,79000 143 | Julian Torres,519-282-3030,4,PhD,Tier3,Senior,No,14/01/1995,130500 144 | Tristan Lawrence,108-639-7444,5,Bsc,Tier2,Senior,Yes,01/07/1973,111500 145 | Laura Rodriguez,528-704-1105,4,PhD,Tier1,Senior,No,30/03/1988,120500 146 | Kristin Freeman,495-440-2789,4,Bsc,Tier1,Mid,No,24/08/2007,79000 147 | Brian Lawson,369-193-1442,3,Bsc,Tier2,Junior,No,05/03/1980,61500 148 | Jeffrey Stephenson,387-025-9585,5,Msc,Tier2,Senior,No,06/06/1995,116500 149 | Holly Allen,317-603-3878,5,Bsc,Tier3,Senior,No,16/04/1974,110500 150 | Carly Gonzalez,251-622-5409,1,Bsc,Tier1,Junior,No,17/10/2010,49500 151 | Jennifer Poole,786-293-8124,5,Msc,Tier1,Senior,No,05/12/2008,112500 152 | Amanda Caldwell,908-486-7080,1,Bsc,Tier3,Junior,Yes,22/02/1986,66500 153 | Shannon Peterson,551-579-2071,2,Bsc,Tier1,Mid,No,23/12/1976,71000 154 | Daniel Brown,228-512-3145,5,Bsc,Tier1,Senior,No,14/12/1989,100500 155 | Brian Preston,434-990-5059,5,PhD,Tier1,Senior,No,02/02/1995,124500 156 | Eric Smith,268-668-9036,5,Bsc,Tier3,Senior,Yes,16/05/1979,117500 157 | Lori Scott,971-670-2994,3,Bsc,Tier1,Junior,No,30/06/2002,57500 158 | Tyler Bailey,420-213-5389,5,Bsc,Tier2,Senior,No,15/10/1987,104500 159 | James Rogers,885-313-9339,4,Bsc,Tier1,Mid,Yes,16/10/1987,86000 160 | Belinda Bennett,290-519-6054,2,PhD,Tier2,Mid,No,17/10/1987,99000 161 | Mr. Anthony Smith,714-543-4659,2,PhD,Tier2,Junior,Yes,18/10/1987,88500 162 | Colton Smith,506-675-8889,1,PhD,Tier3,Mid,Yes,19/10/1987,108000 163 | Benjamin Hernandez,308-402-4852,5,Msc,Tier2,Senior,No,02/12/1975,116500 164 | Katherine Morton,929-838-5203,5,Msc,Tier3,Senior,Yes,30/10/1973,129500 165 | Brent Rollins,264-285-7066,1,Bsc,Tier2,Junior,Yes,29/03/1975,60500 166 | Craig Bell,981-774-1307,3,Msc,Tier2,Mid,Yes,30/03/1975,98000 167 | Ms. Lori Macdonald,816-992-1605,2,Bsc,Tier2,Junior,Yes,30/07/1999,64500 168 | Corey Whitney,895-110-1456,4,PhD,Tier1,Senior,Yes,11/06/1995,127500 169 | Mrs. April Smith MD,134-562-4877,1,PhD,Tier2,Mid,No,19/04/1988,95000 170 | Michael Garcia,340-995-2864,3,Bsc,Tier1,Mid,Yes,26/12/1986,82000 171 | James Hicks,457-272-0458,1,Bsc,Tier1,Junior,Yes,14/07/1984,56500 172 | Benjamin Hinton,122-006-8879,5,Msc,Tier2,Senior,Yes,25/04/2002,123500 173 | Corey Adams,673-984-0439,5,Msc,Tier1,Senior,Yes,23/07/1982,119500 174 | Alyssa Watson,486-576-9615,1,Bsc,Tier2,Junior,Yes,10/06/1998,60500 175 | Paul Jackson,956-383-4712,2,PhD,Tier2,Mid,Yes,11/06/1998,106000 176 | Michael Curry,651-904-7881,5,Msc,Tier1,Senior,No,02/07/1991,112500 177 | Christopher Aguirre,375-500-8085,5,PhD,Tier1,Senior,No,15/11/1994,124500 178 | Elizabeth Howe,883-701-8013,1,Bsc,Tier2,Junior,No,03/12/2003,53500 179 | Jose Adams,101-510-2656,4,Bsc,Tier1,Senior,Yes,10/04/1991,103500 180 | Bryan Sanchez,621-617-2982,5,Bsc,Tier3,Senior,Yes,07/09/1989,117500 181 | Brittany Burke,802-306-8873,1,Bsc,Tier3,Junior,No,14/08/2019,59500 182 | Dalton Miller,724-330-1127,3,Bsc,Tier3,Mid,Yes,27/09/1980,92000 183 | Jason Thompson,265-383-8661,2,Bsc,Tier1,Junior,Yes,28/03/2016,60500 184 | Richard Garza,656-436-2037,3,PhD,Tier2,Mid,No,28/12/1984,103000 185 | Kelly Phillips,548-442-2582,3,Msc,Tier1,Mid,Yes,02/07/2001,94000 186 | Adam Burke,700-693-7205,4,Msc,Tier2,Senior,Yes,28/05/1974,119500 187 | Matthew Sexton,702-746-8081,5,Bsc,Tier1,Senior,Yes,10/02/2010,107500 188 | Erin Jennings,411-686-3551,3,Bsc,Tier1,Junior,No,07/06/2011,57500 189 | Tracy Dyer,440-779-9769,5,PhD,Tier2,Senior,Yes,18/07/2015,135500 190 | Cheryl Singh,373-807-8023,1,PhD,Tier1,Junior,Yes,05/05/1995,80500 191 | Morgan Salinas,355-419-2082,5,Bsc,Tier3,Senior,Yes,09/10/1985,117500 192 | Jose Gardner,557-969-5660,4,Bsc,Tier3,Senior,Yes,10/10/1985,113500 193 | Ricardo Walker,740-183-9013,5,Msc,Tier1,Senior,No,01/05/1975,112500 194 | Kimberly Barrera,381-660-9629,1,Bsc,Tier3,Junior,Yes,25/03/1997,66500 195 | Steven Martin,881-912-7601,2,PhD,Tier1,Mid,Yes,26/03/1997,102000 196 | Eric Taylor,874-969-5189,5,PhD,Tier2,Senior,Yes,23/02/1995,135500 197 | John Roberts,895-902-2827,4,PhD,Tier2,Senior,No,24/02/1995,124500 198 | Morgan Perez,392-951-0998,5,Bsc,Tier3,Senior,No,02/06/1998,110500 199 | Nicole Bright,643-852-1334,4,PhD,Tier2,Senior,Yes,27/10/2004,131500 200 | Emily Castillo,545-724-4767,4,Msc,Tier3,Senior,Yes,16/05/1990,125500 201 | Abigail House,295-134-1618,2,Msc,Tier2,Junior,No,30/11/2001,69500 202 | Justin Adams,479-492-2645,3,Msc,Tier3,Junior,No,29/11/1974,79500 203 | Sarah Rubio,952-257-1708,3,Bsc,Tier3,Junior,No,27/11/2006,67500 204 | Timothy Bishop,143-007-4484,2,Msc,Tier1,Junior,No,23/11/1976,65500 205 | Rachel Ellis,767-614-5474,1,Bsc,Tier1,Junior,No,25/03/1974,49500 206 | Paul Rowe,298-111-2196,5,Bsc,Tier2,Senior,Yes,23/01/1990,111500 207 | Angela Mendoza,469-981-3402,2,PhD,Tier1,Junior,Yes,26/06/1974,84500 208 | Lauren Lewis,164-539-4796,1,Msc,Tier2,Junior,Yes,27/06/1974,72500 209 | Douglas Mora,291-189-6849,4,PhD,Tier2,Senior,No,06/01/1999,124500 210 | Jeffrey Turner,411-884-5206,4,Msc,Tier1,Senior,Yes,27/05/2002,115500 211 | Miss Jamie Mitchell DDS,580-059-9986,1,Bsc,Tier1,Junior,Yes,23/03/1994,56500 212 | John Wilson,851-687-1189,5,Msc,Tier2,Senior,No,13/02/2001,116500 213 | Anna Johnson,230-334-3345,4,Bsc,Tier1,Senior,No,27/02/1976,96500 214 | Brent Bennett,589-271-4426,4,Msc,Tier2,Senior,No,27/11/1988,112500 215 | Jesse Martin,431-864-0720,3,PhD,Tier2,Junior,Yes,29/12/1978,92500 216 | Joseph Schwartz,619-821-7208,2,Bsc,Tier3,Mid,Yes,27/10/2012,88000 217 | Tara Newton,916-946-4615,1,PhD,Tier2,Junior,Yes,03/06/1999,84500 218 | William Carter,256-360-4839,2,Msc,Tier1,Junior,Yes,26/09/1970,72500 219 | Richard Martinez,163-907-0542,3,Msc,Tier2,Mid,No,25/11/1970,91000 220 | Richard Long,417-625-5965,2,Bsc,Tier1,Mid,Yes,03/08/1987,78000 221 | Shannon Brown,835-500-8055,5,Bsc,Tier3,Senior,No,26/08/1984,110500 222 | Katherine Scott,441-237-1746,5,Bsc,Tier1,Senior,No,27/08/1984,100500 223 | Katherine Cook,657-054-4369,2,Msc,Tier2,Junior,Yes,28/08/1984,76500 224 | Lori Garcia,860-230-7451,5,PhD,Tier3,Senior,No,29/08/1984,134500 225 | Larry Schultz,404-144-0034,5,Msc,Tier1,Senior,Yes,28/01/1978,119500 226 | Ricardo Mosley,116-499-4038,3,PhD,Tier2,Junior,No,24/05/2010,85500 227 | James Davenport,389-771-2426,2,PhD,Tier2,Mid,Yes,17/06/2005,106000 228 | Angela Clark,610-326-8007,5,Bsc,Tier2,Senior,No,05/12/1987,104500 229 | Erik Hughes,666-451-1750,5,Bsc,Tier3,Senior,No,06/05/1989,110500 230 | Christine Banks,179-440-0063,5,Msc,Tier1,Senior,Yes,13/04/1978,119500 231 | Bruce Gonzalez,523-661-1686,5,Msc,Tier2,Senior,No,04/02/2003,116500 232 | Mary Williams,375-441-2260,5,Msc,Tier2,Senior,No,27/04/1987,116500 233 | Benjamin Wilson,893-420-4832,4,Bsc,Tier2,Senior,No,10/12/1990,100500 234 | Jason Martinez,819-453-2027,5,Msc,Tier3,Senior,No,19/12/1995,122500 235 | Ryan Chandler,672-186-8101,5,Msc,Tier1,Senior,No,01/01/1993,112500 236 | Valerie Johnson,803-998-8472,3,PhD,Tier2,Mid,Yes,02/01/1993,110000 237 | Connie Smith,620-879-1044,5,Bsc,Tier3,Senior,Yes,03/01/1993,117500 238 | Erin Blair,936-932-1432,1,Bsc,Tier1,Junior,No,04/01/1993,49500 239 | Mark Contreras,535-906-9196,4,Bsc,Tier1,Senior,No,05/01/1993,96500 240 | Deborah Ferguson,177-106-0697,2,Bsc,Tier1,Mid,Yes,06/01/1993,78000 241 | Peter White,719-477-2903,1,Bsc,Tier2,Junior,No,07/01/1993,53500 242 | Philip Russo,517-131-6399,4,Bsc,Tier1,Senior,No,08/01/1993,96500 243 | April Barrett,783-958-0360,2,Bsc,Tier1,Mid,No,09/01/1993,71000 244 | Glen Hall,117-765-0172,1,Msc,Tier1,Junior,No,10/01/1993,61500 245 | Pamela Sanchez,354-264-0499,2,Bsc,Tier3,Mid,Yes,11/01/1993,88000 246 | Adam Graham,107-988-5466,3,PhD,Tier2,Mid,No,12/01/1993,103000 247 | Christopher Brown,376-543-7355,3,Msc,Tier1,Junior,Yes,26/08/2001,76500 248 | Emily Brown,553-413-5579,5,Bsc,Tier1,Senior,Yes,08/02/1980,107500 249 | Mrs. Audrey Ware,578-962-7818,5,PhD,Tier3,Senior,No,11/10/1998,134500 250 | Amanda Cooper,481-825-6149,4,Msc,Tier2,Senior,No,06/04/1993,112500 251 | -------------------------------------------------------------------------------- /Chapter 7/Chapter_7_1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [] 7 | }, 8 | "kernelspec": { 9 | "name": "python3", 10 | "display_name": "Python 3" 11 | }, 12 | "language_info": { 13 | "name": "python" 14 | } 15 | }, 16 | "cells": [ 17 | { 18 | "cell_type": "code", 19 | "execution_count": 18, 20 | "metadata": { 21 | "id": "SPQXH1nxbmiQ" 22 | }, 23 | "outputs": [], 24 | "source": [ 25 | "import numpy as np\n", 26 | "import tensorflow as tf\n" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "source": [ 32 | "(x_train,y_train),(x_test,y_test) = tf.keras.datasets.fashion_mnist.load_data()" 33 | ], 34 | "metadata": { 35 | "id": "cMO4yNtYbr_S" 36 | }, 37 | "execution_count": 8, 38 | "outputs": [] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "source": [ 43 | "len(x_train), len(x_test)" 44 | ], 45 | "metadata": { 46 | "colab": { 47 | "base_uri": "https://localhost:8080/" 48 | }, 49 | "id": "_TmGfTLabtu-", 50 | "outputId": "2db31541-3a2d-4a6a-a778-01645283996b" 51 | }, 52 | "execution_count": 9, 53 | "outputs": [ 54 | { 55 | "output_type": "execute_result", 56 | "data": { 57 | "text/plain": [ 58 | "(60000, 10000)" 59 | ] 60 | }, 61 | "metadata": {}, 62 | "execution_count": 9 63 | } 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "source": [ 69 | "# Reshape the images(batch_size, height, width, channels)\n", 70 | "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1).astype('float32')\n", 71 | "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1).astype('float32')" 72 | ], 73 | "metadata": { 74 | "id": "AjhTq3SXbw_v" 75 | }, 76 | "execution_count": 10, 77 | "outputs": [] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "source": [ 82 | "# Normalize the pixel values\n", 83 | "x_train /= 255\n", 84 | "x_test /= 255" 85 | ], 86 | "metadata": { 87 | "id": "P6hOQMgHb1Z4" 88 | }, 89 | "execution_count": 11, 90 | "outputs": [] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "source": [ 95 | "# Convert the labels to one hot encoding format\n", 96 | "y_train = tf.keras.utils.to_categorical(y_train, 10)\n", 97 | "y_test = tf.keras.utils.to_categorical(y_test, 10)" 98 | ], 99 | "metadata": { 100 | "id": "CNdGjzetb3kv" 101 | }, 102 | "execution_count": null, 103 | "outputs": [] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "source": [ 108 | "# Build the Sequential model\n", 109 | "model = tf.keras.models.Sequential()\n", 110 | "# Add convolutional layer\n", 111 | "model.add(tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n", 112 | "# Add max pooling layer\n", 113 | "model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n", 114 | "# Flatten the data\n", 115 | "model.add(tf.keras.layers.Flatten())\n", 116 | "# Add fully connected layer\n", 117 | "model.add(tf.keras.layers.Dense(128, activation='relu'))\n", 118 | "# Apply softmax\n", 119 | "model.add(tf.keras.layers.Dense(10, activation='softmax'))\n" 120 | ], 121 | "metadata": { 122 | "id": "Zze1cSsab3iQ" 123 | }, 124 | "execution_count": 13, 125 | "outputs": [] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "source": [ 130 | "# Compile and fit the model\n", 131 | "model.compile(loss='categorical_crossentropy',\n", 132 | " optimizer='adam', metrics=['accuracy'])\n", 133 | "model.fit(x_train, y_train, epochs=10, validation_split=0.2)" 134 | ], 135 | "metadata": { 136 | "colab": { 137 | "base_uri": "https://localhost:8080/" 138 | }, 139 | "id": "2sC2Pcahb3eY", 140 | "outputId": "22b6a4a1-8149-4042-90d8-938818e6d03d" 141 | }, 142 | "execution_count": 14, 143 | "outputs": [ 144 | { 145 | "output_type": "stream", 146 | "name": "stdout", 147 | "text": [ 148 | "Epoch 1/10\n", 149 | "1500/1500 [==============================] - 31s 20ms/step - loss: 0.3972 - accuracy: 0.8584 - val_loss: 0.3135 - val_accuracy: 0.8857\n", 150 | "Epoch 2/10\n", 151 | "1500/1500 [==============================] - 39s 26ms/step - loss: 0.2611 - accuracy: 0.9054 - val_loss: 0.2661 - val_accuracy: 0.9027\n", 152 | "Epoch 3/10\n", 153 | "1500/1500 [==============================] - 29s 19ms/step - loss: 0.2178 - accuracy: 0.9196 - val_loss: 0.2709 - val_accuracy: 0.9016\n", 154 | "Epoch 4/10\n", 155 | "1500/1500 [==============================] - 30s 20ms/step - loss: 0.1840 - accuracy: 0.9320 - val_loss: 0.2445 - val_accuracy: 0.9109\n", 156 | "Epoch 5/10\n", 157 | "1500/1500 [==============================] - 30s 20ms/step - loss: 0.1551 - accuracy: 0.9427 - val_loss: 0.2437 - val_accuracy: 0.9178\n", 158 | "Epoch 6/10\n", 159 | "1500/1500 [==============================] - 30s 20ms/step - loss: 0.1326 - accuracy: 0.9520 - val_loss: 0.2462 - val_accuracy: 0.9172\n", 160 | "Epoch 7/10\n", 161 | "1500/1500 [==============================] - 29s 20ms/step - loss: 0.1092 - accuracy: 0.9594 - val_loss: 0.2740 - val_accuracy: 0.9165\n", 162 | "Epoch 8/10\n", 163 | "1500/1500 [==============================] - 30s 20ms/step - loss: 0.0945 - accuracy: 0.9650 - val_loss: 0.2913 - val_accuracy: 0.9174\n", 164 | "Epoch 9/10\n", 165 | "1500/1500 [==============================] - 30s 20ms/step - loss: 0.0775 - accuracy: 0.9720 - val_loss: 0.3125 - val_accuracy: 0.9119\n", 166 | "Epoch 10/10\n", 167 | "1500/1500 [==============================] - 30s 20ms/step - loss: 0.0648 - accuracy: 0.9761 - val_loss: 0.3082 - val_accuracy: 0.9168\n" 168 | ] 169 | }, 170 | { 171 | "output_type": "execute_result", 172 | "data": { 173 | "text/plain": [ 174 | "" 175 | ] 176 | }, 177 | "metadata": {}, 178 | "execution_count": 14 179 | } 180 | ] 181 | }, 182 | { 183 | "cell_type": "code", 184 | "source": [ 185 | "model.summary()" 186 | ], 187 | "metadata": { 188 | "colab": { 189 | "base_uri": "https://localhost:8080/" 190 | }, 191 | "id": "n1XafoNhcIiK", 192 | "outputId": "212ec2e6-430a-4910-e4e7-668d5addbc97" 193 | }, 194 | "execution_count": 15, 195 | "outputs": [ 196 | { 197 | "output_type": "stream", 198 | "name": "stdout", 199 | "text": [ 200 | "Model: \"sequential\"\n", 201 | "_________________________________________________________________\n", 202 | " Layer (type) Output Shape Param # \n", 203 | "=================================================================\n", 204 | " conv2d (Conv2D) (None, 26, 26, 64) 640 \n", 205 | " \n", 206 | " max_pooling2d (MaxPooling2 (None, 13, 13, 64) 0 \n", 207 | " D) \n", 208 | " \n", 209 | " flatten (Flatten) (None, 10816) 0 \n", 210 | " \n", 211 | " dense (Dense) (None, 128) 1384576 \n", 212 | " \n", 213 | " dense_1 (Dense) (None, 10) 1290 \n", 214 | " \n", 215 | "=================================================================\n", 216 | "Total params: 1386506 (5.29 MB)\n", 217 | "Trainable params: 1386506 (5.29 MB)\n", 218 | "Non-trainable params: 0 (0.00 Byte)\n", 219 | "_________________________________________________________________\n" 220 | ] 221 | } 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "source": [ 227 | "test_loss, test_acc=model.evaluate(x_test,y_test)\n", 228 | "print('Test Accuracy: ', test_acc)" 229 | ], 230 | "metadata": { 231 | "colab": { 232 | "base_uri": "https://localhost:8080/" 233 | }, 234 | "id": "TiMX83lJc3BP", 235 | "outputId": "6a5d283b-ee07-44bd-aaac-b9da2705071c" 236 | }, 237 | "execution_count": 17, 238 | "outputs": [ 239 | { 240 | "output_type": "stream", 241 | "name": "stdout", 242 | "text": [ 243 | "313/313 [==============================] - 2s 5ms/step - loss: 0.3283 - accuracy: 0.9111\n", 244 | "Test Accuracy: 0.9110999703407288\n" 245 | ] 246 | } 247 | ] 248 | } 249 | ] 250 | } -------------------------------------------------------------------------------- /Chapter 7/weather dataset.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/TensorFlow-Developer-Certificate-Guide/b23fbe47f16a3b4023bbab5ae08e9e5a98e9f975/Chapter 7/weather dataset.zip -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Packt 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # TensorFlow Developer Certificate Guide 2 | 3 | TensorFlow Developer Certificate Guide 4 | 5 | This is the code repository for [TensorFlow Developer Certificate Guide](https://www.packtpub.com/product/tensorflow-developer-certificate-guide/9781803240138), published by Packt. 6 | 7 | **Efficiently tackle deep learning and ML problems to ace the Developer Certificate exam** 8 | 9 | ## What is this book about? 10 | The TensorFlow Developer Certificate Guide is an indispensable resource for machine learning enthusiasts and data professionals seeking to master TensorFlow and validate their skills by earning the certification. This practical guide equips you with the skills and knowledge necessary to build robust deep learning models that effectively tackle real-world challenges across diverse industries. 11 | You’ll embark on a journey of skill acquisition through easy-to-follow, step-by-step explanations and practical examples, mastering the craft of building sophisticated models using TensorFlow 2.x and overcoming common hurdles such as overfitting and data augmentation. With this book, you’ll discover a wide range of practical applications, including computer vision, natural language processing, and time series prediction. 12 | 13 | This book covers the following exciting features: 14 | * Prepare for success in the TensorFlow Developer Certification exam 15 | * Master regression and classification modelling with TensorFlow 2.x 16 | * Build, train, evaluate, and fine-tune deep learning models 17 | * Combat overfitting using techniques such as dropout and data augmentation 18 | * Classify images, encompassing preprocessing and image data augmentation 19 | * Apply TensorFlow for NLP tasks like text classification and generation 20 | * Predict time series data, such as stock prices 21 | * Explore real-world case studies and engage in hands-on exercises 22 | 23 | If you feel this book is for you, get your [copy](https://www.amazon.com/dp/180324013X) today! 24 | 25 | https://www.packtpub.com/ 26 | 27 | ## Instructions and Navigations 28 | All of the code is organized into folders. 29 | 30 | The code will look like the following: 31 | ``` 32 | import numpy as np 33 | from numpy import 34 | ``` 35 | 36 | **Following is what you need for this book:** 37 | This book is for machine learning and data science enthusiasts, as well as data professionals aiming to demonstrate their expertise in building deep learning applications with TensorFlow. Through a comprehensive hands-on approach, this book covers all the essential exam prerequisites to equip you with the skills needed to excel as a TensorFlow developer and advance your career in machine learning. A fundamental grasp of Python programming is the only prerequisite. 38 | 39 | With the following software and hardware list you can run all code files present in the book (Chapter 1-13). 40 | 41 | ### Software and Hardware List 42 | 43 | | Chapter | Software required | OS required | 44 | | -------- | -------------------------------------------------------------------------------------| -----------------------------------| 45 | | 1-13 | Python 3.9.2 | Windows, Mac OS X, and Linux (Any) | 46 | |1-13 |tensorflow==2.13.0 | Windows, Mac OS X, and Linux (Any) | 47 | |1-13| numpy==1.24.3 | Windows, Mac OS X, and Linux (Any) | 48 | |1-13 | pandas==2.0.3 | Windows, Mac OS X, and Linux (Any) | 49 | |1-13 |Pillow==10.0.0 | Windows, Mac OS X, and Linux (Any) | 50 | |1-13 |scipy==1.10.1 | Windows, Mac OS X, and Linux (Any) | 51 | |1-13 |tensorflow-datasets==4.9.2 | Windows, Mac OS X, and Linux (Any) | 52 | 53 | 54 | ### Related products 55 | * Hands-On Image Generation with TensorFlow [[Packt]](https://www.packtpub.com/product/hands-on-image-generation-with-tensorflow/9781838826789) [[Amazon]](https://www.amazon.in/Hands-Image-Generation-TensorFlow-generating/dp/1838826785) 56 | 57 | * TensorFlow 2.0 Computer Vision Cookbook [[Packt]](https://www.packtpub.com/product/tensorflow-20-computer-vision-cookbook/9781838829131) [[Amazon]](https://www.amazon.in/TensorFlow-2-0-Computer-Vision-Cookbook/dp/183882913X) 58 | 59 | ## Get to Know the Author 60 | **Oluwole Fagbohun** a certified TensorFlow developer and machine learning engineer, currently he is serving Vice President of Engineering and Environmental Data at ChangeBlock, where he is spearheading the fight against climate change with machine learning. Beyond his role at ChangeBlock, Oluwole is the founder of Readrly an EdTech startup employing AI to enhance children's reading skills, offering engaging and gamified learning experiences. 61 | --------------------------------------------------------------------------------