├── .gitignore ├── 1.Neural Networks and Deep Learning ├── 1.Intro to Deep Learning │ └── Introduction to Deep Learning.ipynb ├── 2.Neural Networks Basics │ ├── Assignment1.ipynb │ ├── Neural Networks Basics.ipynb │ ├── __pycache__ │ │ └── lr_utils.cpython-36.pyc │ ├── datasets │ │ ├── test_catvnoncat.h5 │ │ └── train_catvnoncat.h5 │ ├── images │ │ ├── LogReg_kiank.png │ │ ├── cat_in_iran.jpg │ │ ├── gargouille.jpg │ │ ├── image1.png │ │ ├── image2.png │ │ ├── la_defense.jpg │ │ ├── my_image.jpg │ │ └── my_image2.jpg │ └── lr_utils.py ├── 3.Shallow Neural Networks │ ├── Planar Data Classification With One Hidden Layer.ipynb │ ├── Shallow Neural Network.ipynb │ ├── __pycache__ │ │ ├── planar_utils.cpython-36.pyc │ │ └── testCases.cpython-36.pyc │ ├── planar_utils.py │ └── testCases.py └── 4.Deep Neural Networks │ ├── Building a Deep Neural Network.ipynb │ ├── Deep Neural Network - Application.ipynb │ ├── __pycache__ │ ├── dnn_app_utils_v2.cpython-36.pyc │ ├── dnn_utils_v2.cpython-36.pyc │ └── testCases_v2.cpython-36.pyc │ ├── datasets │ ├── test_catvnoncat.h5 │ └── train_catvnoncat.h5 │ ├── dnn_app_utils_v2.py │ ├── dnn_utils_v2.py │ ├── my_image.jpg │ └── testCases_v2.py ├── 2.Improving Deep Neural Networks ├── 1.Practical Aspects of Deep Learning │ ├── Gradient Checking.ipynb │ ├── Initialization.ipynb │ ├── Regularization.ipynb │ ├── __pycache__ │ │ ├── gc_utils.cpython-36.pyc │ │ ├── init_utils.cpython-36.pyc │ │ ├── reg_utils.cpython-36.pyc │ │ └── testCases.cpython-36.pyc │ ├── datasets │ │ ├── data.mat │ │ ├── test_catvnoncat.h5 │ │ └── train_catvnoncat.h5 │ ├── gc_utils.py │ ├── init_utils.py │ ├── reg_utils.py │ └── testCases.py ├── 2.Algorithm Optimization │ ├── Optimization Methods.ipynb │ ├── __pycache__ │ │ ├── opt_utils.cpython-36.pyc │ │ └── testCases.cpython-36.pyc │ ├── datasets │ │ └── data.mat │ ├── opt_utils.py │ └── testCases.py └── 3.Hyperparameter Tuning, Batch Normalization and Programming Frameworks │ ├── Tensorflow Application.ipynb │ ├── Tensorflow Tutorial.ipynb │ ├── __pycache__ │ └── tf_utils.cpython-36.pyc │ ├── datasets │ ├── test_signs.h5 │ └── train_signs.h5 │ ├── improv_utils.py │ ├── tf_utils.py │ └── thumbs_up.jpg ├── 3.Structuring Machine Learning Projects └── Structuring Machine Learning Project.ipynb ├── 4.Convolutional Neural Networks ├── 1.Foundations of Convolutional Neural Networks │ ├── ConvNet in TensorFlow.ipynb │ ├── Convolutional Neural Network - Step by Step.ipynb │ ├── __pycache__ │ │ └── cnn_utils.cpython-36.pyc │ ├── cnn_utils.py │ └── datasets │ │ ├── test_signs.h5 │ │ └── train_signs.h5 ├── 2.Deep Convolutional Models │ ├── HappyModel.png │ ├── Keras Tutorial.ipynb │ ├── ResNet.png │ ├── Residual Networks.ipynb │ ├── __pycache__ │ │ ├── kt_utils.cpython-36.pyc │ │ └── resnets_utils.cpython-36.pyc │ ├── datasets │ │ ├── test_happy.h5 │ │ ├── test_signs.h5 │ │ ├── train_happy.h5 │ │ └── train_signs.h5 │ ├── kt_utils.py │ └── resnets_utils.py ├── 4.Special Applications - Facial Recognition & Neural Style Transfer │ ├── Neural Style Transfer.ipynb │ ├── datasets │ │ ├── test_happy.h5 │ │ ├── train_face.h5 │ │ └── train_happy.h5 │ ├── fr_utils.py │ ├── inception_blocks.py │ ├── nn4.small2.v7.h5 │ └── nst_utils.py └── Object Detection │ ├── YOLO Implementation.ipynb │ └── sample.txt └── 5.Sequence Models ├── 1.Recurrent Neural Networks ├── Building a Recurrent Neural Network From Scratch.ipynb ├── Dinosoraus Land.ipynb ├── Improvise a Jazz Solo with LSTM.ipynb ├── data_utils.py ├── dinos.txt ├── grammar.py ├── music_utils.py ├── my_music.midi ├── preprocess.py ├── qa.py ├── rnn_utils.py ├── sample.txt ├── shakespeare.txt ├── shakespeare_utils.py └── utils.py ├── 2.Natural Language Processing & Word Embeddings ├── Emojify.ipynb ├── Remove Bias.ipynb ├── emo_utils.py ├── sample.txt └── w2v_utils.py └── 3.Sequence Models & Attention Mechanism ├── Neural Machine Translation.ipynb ├── Trigger+word+detection+-+v1.ipynb ├── nmt_utils.py └── sample.txt /.gitignore: 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