A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
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BatchNorm before and after Activation for Network-in-Network CIFAR-10 Classifier
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Filter Response Normalization for Network-in-Network CIFAR-10 Classifier
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Convolutional Autoencoder with Deconvolutions / Transposed Convolutions
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Convolutional Autoencoder with Deconvolutions and Continuous Jaccard Distance
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Convolutional Autoencoder with Deconvolutions (without pooling operations)
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Convolutional Autoencoder with Nearest-neighbor Interpolation
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Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on CelebA
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Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on Quickdraw
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Conditional Variational Autoencoder (with labels in reconstruction loss)
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Conditional Variational Autoencoder (without labels in reconstruction loss)
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Convolutional Conditional Variational Autoencoder (with labels in reconstruction loss)
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Convolutional Conditional Variational Autoencoder (without labels in reconstruction loss)
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Most Basic Graph Neural Network with Gaussian Filter on MNIST
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Basic Graph Neural Network with Spectral Graph Convolution on MNIST
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A simple single-layer RNN with packed sequences to ignore padding characters (IMDB)
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RNN with LSTM cells (IMDB) and pre-trained GloVe word vectors
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Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (AG News)
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Ordinal Regression CNN -- Niu et al. 2016 w. ResNet34 on AFAD-Lite
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Ordinal Regression CNN -- Beckham and Pal 2016 w. ResNet34 on AFAD-Lite
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Vanilla Loss Gradient (wrt Inputs) Visualization (Based on a VGG16 Convolutional Neural Network for Kaggle's Cats and Dogs Images)
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Guided Backpropagation (Based on a VGG16 Convolutional Neural Network for Kaggle's Cats and Dogs Images)
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MLP in Lightning with TensorBoard -- continue training the last model
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MLP in Lightning with TensorBoard -- checkpointing best model
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Using PyTorch Dataset Loading Utilities for Custom Datasets -- CSV files converted to HDF5
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Using PyTorch Dataset Loading Utilities for Custom Datasets -- Face Images from CelebA
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Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from Quickdraw
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Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from the Street View House Number (SVHN) Dataset
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Using PyTorch Dataset Loading Utilities for Custom Datasets -- Asian Face Dataset (AFAD)
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Using PyTorch Dataset Loading Utilities for Custom Datasets -- Dating Historical Color Images
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Using PyTorch Dataset Loading Utilities for Custom Datasets -- Fashion MNIST
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Using Multiple GPUs with DataParallel -- VGG-16 Gender Classifier on CelebA
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Distribute a Model Across Multiple GPUs with Pipeline Parallelism (VGG-16 Example)
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PyTorch with and without Deterministic Behavior -- Runtime Benchmark
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Plotting Live Training Performance in Jupyter Notebooks with just Matplotlib