├── P0_Titanic ├── Kaggle_Titanic │ ├── gender_submission.csv │ ├── output.csv │ ├── test.csv │ ├── titanic.py │ └── train.csv ├── README.md ├── Titanic_Decision_Tree_Solution.png ├── p0_playground.ipynb ├── titanic_data.csv ├── titanic_survival_exploration.html ├── titanic_survival_exploration.ipynb └── titanic_visualizations.py ├── P1_Boston_Housing ├── README.md ├── bj_housing.csv ├── boston_housing.html ├── boston_housing.ipynb ├── code.py ├── code_bj.py ├── housing.csv └── visuals.py ├── P2_Finding_Donors ├── Mini_Project_NLP.py ├── README.md ├── census.csv ├── code.py ├── finding_donors.html ├── finding_donors.ipynb ├── p2_playground.ipynb ├── project_description.md ├── tree.png └── visuals.py ├── P3_Create_Customer_Segments ├── Mini_Project_Eigenfaces.py ├── PCA - GMM preds.png ├── PCA - GMM preds_prob.png ├── README.md ├── Visualization Samples .ipynb ├── cluster.csv ├── customer_segments.html ├── customer_segments.ipynb ├── customers.csv └── visuals.py ├── P4_Smart_Cab ├── README.md ├── images │ ├── car-black.png │ ├── car-blue.png │ ├── car-cyan.png │ ├── car-green.png │ ├── car-magenta.png │ ├── car-orange.png │ ├── car-red.png │ ├── car-white.png │ ├── car-yellow.png │ ├── east-west.png │ ├── logo.png │ └── north-south.png ├── logs │ ├── sim_default-learning (4 Features, Training 20 rounds, Score FA).csv │ ├── sim_default-learning (4 Features, Training 20 rounds, Score FA).png │ ├── sim_default-learning (4 Features, Training 20 rounds, Score FA).txt │ ├── sim_default-learning (5 Features, Training 20 rounds, Score FB).csv │ ├── sim_default-learning (5 Features, Training 20 rounds, Score FB).png │ ├── sim_default-learning (5 Features, Training 20 rounds, Score FB).txt │ ├── sim_default-learning (5 Features, Training 20 rounds, Score FD).csv │ ├── sim_default-learning (5 Features, Training 20 rounds, Score FD).png │ ├── sim_default-learning (5 Features, Training 20 rounds, Score FD).txt │ ├── sim_improved-learning - Use decay_exponential (4 Features, 300 rounds, 310 combinations, Score A+A+).csv │ ├── sim_improved-learning - Use decay_exponential (4 Features, 300 rounds, 310 combinations, Score A+A+).png │ ├── sim_improved-learning - Use decay_exponential (4 Features, 300 rounds, 310 combinations, Score A+A+).txt │ ├── sim_improved-learning - Use decay_exponential (5 Features, 600 rounds, 1000 combinations, Score A+A).csv │ ├── sim_improved-learning - Use decay_exponential (5 Features, 600 rounds, 1000 combinations, Score A+A).png │ ├── sim_improved-learning - Use decay_exponential (5 Features, 600 rounds, 1000 combinations, Score A+A).txt │ ├── sim_improved-learning - Use decay_exponential (5 Features, 600 rounds, 1000 combinations, Score A+A+).csv │ ├── sim_improved-learning - Use decay_exponential (5 Features, 600 rounds, 1000 combinations, Score A+A+).png │ ├── sim_improved-learning - Use decay_exponential (5 Features, 600 rounds, 1000 combinations, Score A+A+).txt │ ├── sim_improved-learning - Use decay_step (4 Features, 300 rounds, 370 combinations, Score A+A+).csv │ ├── sim_improved-learning - Use decay_step (4 Features, 300 rounds, 370 combinations, Score A+A+).png │ ├── sim_improved-learning - Use decay_step (4 Features, 300 rounds, 370 combinations, Score A+A+).txt │ ├── sim_improved-learning - Use decay_step (5 Features, 600 rounds, 1260 combinations, Score A+A+).csv │ ├── sim_improved-learning - Use decay_step (5 Features, 600 rounds, 1260 combinations, Score A+A+).png │ ├── sim_improved-learning - Use decay_step (5 Features, 600 rounds, 1260 combinations, Score A+A+).txt │ ├── sim_no-learning - Hard_coded (No Training, Score A+A+).csv │ ├── sim_no-learning - Hard_coded (No Training, Score A+A+).png │ ├── sim_no-learning.csv │ └── sim_no-learning.png ├── project_description.md ├── smartcab.html ├── smartcab.ipynb ├── smartcab │ ├── __init__.py │ ├── agent.py │ ├── environment.py │ ├── planner.py │ └── simulator.py └── visuals.py ├── P5_Image_Classification ├── CIFAR10 Image Classification - LSUV Initialization.ipynb ├── CIFAR10 Image Classification using Keras (Concise).ipynb ├── CIFAR10 Image Classification using Keras.ipynb ├── CIFAR10 Image Classification using TensorFlow.ipynb ├── CIFAR10 with Transfer Learning (For Fun).ipynb ├── Conv2D.ipynb ├── Intro │ ├── image │ │ ├── Learn_Rate_Tune_Image.png │ │ ├── Mean_Variance_Image.png │ │ ├── network_diagram.png │ │ └── notmnist.png │ └── intro_to_tensorflow.ipynb ├── MNIST Image Classification using Keras.ipynb ├── MNIST │ └── multilayer_perceptron.py ├── README.md ├── castle.jpg ├── helper.py ├── image_classification.ipynb ├── image_classification_ZH-CN.html ├── image_classification_ZH-CN.ipynb ├── lsuv_init.py ├── p5_playground.ipynb ├── problem_unittests.py └── utils.py ├── P6_Dogs_VS_Cats ├── Class Activation Map Visualizations.ipynb ├── Dog VS Cat - Experimenting With Transfer Learning.ipynb ├── Dog VS Cat - Kaggle.ipynb ├── Dog VS Cat using Transfer Learning.ipynb ├── Dogs VS Cats.ipynb ├── Hand-built CNN Model.ipynb ├── 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