├── .gitignore ├── LICENSE ├── README.md ├── attention ├── Attention_Basics.ipynb ├── Attention_Basics_Solution.ipynb └── images │ ├── Context_vector.png │ ├── scoring_functions.png │ └── softmax.png ├── autoencoder ├── convolutional-autoencoder │ ├── Convolutional_Autoencoder_Exercise.ipynb │ ├── Convolutional_Autoencoder_Solution.ipynb │ ├── Upsampling_Solution.ipynb │ └── notebook_ims │ │ ├── autoencoder_1.png │ │ ├── autoencoder_denoise.png │ │ ├── compressed.png │ │ ├── conv_enc_1.png │ │ ├── conv_enc_2.png │ │ ├── convolutional_autoencoder.png │ │ ├── denoising.png │ │ └── mnist_examples.png ├── denoising-autoencoder │ ├── Denoising_Autoencoder_Exercise.ipynb │ ├── Denoising_Autoencoder_Solution.ipynb │ └── notebook_ims │ │ ├── autoencoder_1.png │ │ ├── autoencoder_denoise.png │ │ ├── compressed.png │ │ ├── denoising.png │ │ └── mnist_examples.png └── linear-autoencoder │ ├── Simple_Autoencoder_Exercise.ipynb │ ├── Simple_Autoencoder_Solution.ipynb │ └── notebook_ims │ ├── autoencoder_1.png │ ├── autoencoder_denoise.png │ ├── compressed.png │ ├── denoising.png │ ├── mnist_examples.png │ └── simple_autoencoder.png ├── batch-norm └── Batch_Normalization.ipynb ├── convolutional-neural-networks ├── cifar-cnn │ ├── cifar10_cnn_augmentation.ipynb │ ├── cifar10_cnn_exercise.ipynb │ ├── cifar10_cnn_solution.ipynb │ ├── model_augmented.pt │ ├── model_cifar.pt │ └── notebook_ims │ │ ├── 2_layer_conv.png │ │ └── cifar_data.png ├── conv-visualization │ ├── conv_visualization.ipynb │ ├── custom_filters.ipynb │ ├── data │ │ ├── bridge_trees_example.jpg │ │ ├── curved_lane.jpg │ │ ├── sobel_ops.png │ │ ├── udacity_sdc.png │ │ └── white_lines.jpg │ ├── maxpooling_visualization.ipynb │ └── notebook_ims │ │ ├── CNN_all_layers.png │ │ ├── conv_layer.gif │ │ ├── maxpooling_ex.png │ │ ├── relu_ex.png │ │ └── sobel_ops.png └── mnist-mlp │ ├── mnist_mlp_exercise.ipynb │ ├── mnist_mlp_solution.ipynb │ ├── mnist_mlp_solution_with_validation.ipynb │ └── model.pt ├── cycle-gan ├── CycleGAN_Exercise.ipynb ├── CycleGAN_Solution.ipynb ├── helpers.py ├── notebook_images │ ├── CycleGAN_loss.png │ ├── XY_season_images.png │ ├── cycle_consistency_ex.png │ ├── cyclegan_generator_ex.png │ ├── discriminator_layers.png │ ├── horse2zebra.jpg │ ├── reconstruction_error.png │ ├── resnet_50.png │ ├── resnet_block.png │ ├── sample-004000-summer2winter.png │ └── training_cycle_ex.png └── samples_cyclegan │ └── samples_dir.txt ├── dcgan-svhn ├── DCGAN_Exercise.ipynb ├── DCGAN_Solution.ipynb └── assets │ ├── SVHN_examples.png │ ├── conv_discriminator.png │ ├── conv_generator.png │ └── svhn_dcgan.png ├── gan-mnist ├── MNIST_GAN_Exercise.ipynb ├── MNIST_GAN_Solution.ipynb └── assets │ ├── gan_network.png │ ├── gan_pipeline.png │ ├── leaky_relu.png │ └── tanh_fn.png ├── intro-neural-networks ├── gradient-descent │ ├── GradientDescent.ipynb │ ├── GradientDescentSolutions.ipynb │ └── data.csv └── student-admissions │ ├── .ipynb_checkpoints │ ├── StudentAdmissions-checkpoint.ipynb │ └── StudentAdmissionsSolutions-checkpoint.ipynb │ ├── StudentAdmissions.ipynb │ ├── StudentAdmissionsSolutions.ipynb │ └── student_data.csv ├── intro-to-pytorch ├── Part 1 - Tensors in PyTorch (Exercises).ipynb ├── Part 1 - Tensors in PyTorch (Solution).ipynb ├── Part 2 - Neural Networks in PyTorch (Exercises).ipynb ├── Part 2 - Neural Networks in PyTorch (Solution).ipynb ├── Part 3 - Training Neural Networks (Exercises).ipynb ├── Part 3 - Training Neural Networks (Solution).ipynb ├── Part 4 - Fashion-MNIST (Exercises).ipynb ├── Part 4 - Fashion-MNIST (Solution).ipynb ├── Part 5 - Inference and Validation (Exercises).ipynb ├── Part 5 - Inference and Validation (Solution).ipynb ├── Part 6 - Saving and Loading Models.ipynb ├── Part 7 - Loading Image Data (Exercises).ipynb ├── Part 7 - Loading Image Data (Solution).ipynb ├── Part 8 - Transfer Learning (Exercises).ipynb ├── Part 8 - Transfer Learning (Solution).ipynb ├── README.md ├── assets │ ├── ImageNet_example.png │ ├── Pooling_Simple_max.png │ ├── activation.png │ ├── autoencoder_1.png │ ├── backprop_diagram.png │ ├── cat.70.jpg │ ├── cat_cropped.png │ ├── conv_net.jpg │ ├── dog.128.jpg │ ├── dog_cat.png │ ├── examples_new.png │ ├── fashion-mnist-sprite.png │ ├── full_padding_no_strides_transposed.gif │ ├── function_approx.png │ ├── gradient_descent.png │ ├── image_distribution.png │ ├── infographic.pdf │ ├── lenet.png │ ├── mlp_mnist.png │ ├── mnist.png │ ├── multilayer_diagram_weights.png │ ├── network_diagram.png │ ├── overfitting.png │ ├── padding_strides.gif │ ├── simple_neuron.png │ ├── tensor_examples.svg │ ├── test_examples.png │ ├── train_examples.png │ └── w1_backprop_graph.png ├── fc_model.py └── helper.py ├── keras ├── IMDB-keras │ ├── IMDB_In_Keras.ipynb │ └── IMDB_In_Keras_Solutions.ipynb ├── cifar10-augmentation │ ├── aug_model.weights.best.hdf5 │ └── cifar10_augmentation.ipynb ├── cifar10-classification │ ├── MLP.weights.best.hdf5 │ ├── cifar10_cnn.ipynb │ ├── cifar10_mlp.ipynb │ └── model.weights.best.hdf5 ├── mnist-mlp │ ├── mnist.model.best.hdf5 │ └── mnist_mlp.ipynb ├── student-admissions-keras │ ├── StudentAdmissionsKeras.ipynb │ └── student_data.csv └── transfer-learning │ ├── bottleneck_features.ipynb │ ├── bottleneck_features │ └── .gitignore │ ├── dogvgg16.weights.best.hdf5 │ ├── figures │ ├── vgg16.png │ └── vgg16_transfer.png │ ├── images │ ├── American_water_spaniel_00648.jpg │ ├── Brittany_02625.jpg │ ├── Curly-coated_retriever_03896.jpg │ ├── Labrador_retriever_06449.jpg │ ├── Labrador_retriever_06455.jpg │ ├── Labrador_retriever_06457.jpg │ ├── Welsh_springer_spaniel_08203.jpg │ └── sopa.jpg │ └── transfer_learning.ipynb ├── project-bikesharing ├── .udacity-pa │ └── projects.py ├── Bike-Sharing-Dataset │ ├── Readme.txt │ ├── day.csv │ └── hour.csv ├── Predicting_bike_sharing_data.ipynb ├── assets │ └── neural_network.png └── my_answers.py ├── project-dog-classification ├── README.md ├── dog_app.ipynb ├── haarcascades │ └── haarcascade_frontalface_alt.xml └── images │ ├── American_water_spaniel_00648.jpg │ ├── Brittany_02625.jpg │ ├── Curly-coated_retriever_03896.jpg │ ├── Labrador_retriever_06449.jpg │ ├── Labrador_retriever_06455.jpg │ ├── Labrador_retriever_06457.jpg │ ├── Welsh_springer_spaniel_08203.jpg │ ├── sample_cnn.png │ ├── sample_dog_output.png │ └── sample_human_output.png ├── project-face-generation ├── assets │ └── processed_face_data.png ├── dlnd_face_generation.ipynb └── problem_unittests.py ├── project-tv-script-generation ├── data │ └── Seinfeld_Scripts.txt ├── dlnd_tv_script_generation.ipynb ├── helper.py └── problem_unittests.py ├── recurrent-neural-networks ├── char-rnn │ ├── Character_Level_RNN_Exercise.ipynb │ ├── Character_Level_RNN_Solution.ipynb │ ├── assets │ │ ├── charRNN.png │ │ ├── charRNN@0.5x.png │ │ ├── charseq.jpeg │ │ ├── sequence_batching.png │ │ ├── sequence_batching@1x.png │ │ └── sequence_batching_ex.png │ └── data │ │ └── anna.txt └── time-series │ ├── Simple_RNN.ipynb │ └── assets │ └── time_prediction.png ├── requirements.txt ├── sentiment-analysis-network ├── Sentiment_Classification_Projects.ipynb ├── Sentiment_Classification_Solutions.ipynb ├── Sentiment_Classification_Solutions_2_Better_Weight_Initialization.ipynb ├── labels.txt ├── reviews.txt ├── sentiment_network.png ├── sentiment_network_2.png ├── sentiment_network_pos.png ├── sentiment_network_sparse.png └── sentiment_network_sparse_2.png ├── sentiment-rnn ├── Sentiment_RNN_Exercise.ipynb ├── Sentiment_RNN_Solution.ipynb ├── assets │ ├── network_diagram.png │ ├── outliers_padding_ex.png │ └── reviews_ex.png └── data │ ├── labels.txt │ └── reviews.txt ├── style-transfer ├── Style_Transfer_Exercise.ipynb ├── Style_Transfer_Solution.ipynb ├── images │ ├── ben_passmore.jpg │ ├── delaunay.jpg │ ├── hockney.jpg │ ├── janelle.png │ ├── kahlo.jpg │ ├── magritte.jpg │ ├── octavia.jpg │ ├── octopus.jpg │ ├── sotomayor.jpg │ └── space_needle.jpg └── notebook_ims │ ├── style_tx_cat.png │ └── vgg19_convlayers.png ├── tensorflow └── intro-to-tensorflow │ ├── environment.yml │ ├── environment_win.yml │ ├── image │ ├── Learn_Rate_Tune_Image.png │ ├── Mean_Variance_Image.png │ ├── network_diagram.png │ └── notmnist.png │ ├── intro_to_tensorflow.ipynb │ ├── intro_to_tensorflow_solution.ipynb │ └── requirements.txt ├── transfer-learning ├── Transfer_Learning_Exercise.ipynb ├── Transfer_Learning_Solution.ipynb └── notebook_ims │ └── vgg_16_architecture.png ├── weight-initialization ├── helpers.py ├── notebook_ims │ ├── loss_comparison_ex.png │ ├── neural_net.png │ └── neuron_weights.png ├── weight_initialization_exercise.ipynb └── weight_initialization_solution.ipynb └── word2vec-embeddings ├── Negative_Sampling_Exercise.ipynb ├── Negative_Sampling_Solution.ipynb ├── Skip_Grams_Exercise.ipynb ├── Skip_Grams_Solution.ipynb ├── assets ├── context_drink.png ├── lookup_matrix.png ├── neg_sampling_loss.png ├── one_hot_encoding.png ├── skip_gram_arch.png ├── 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