A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
- Perceptron [TensorFlow 1] [PyTorch]
- Logistic Regression [TensorFlow 1] [PyTorch]
- Softmax Regression (Multinomial Logistic Regression) [TensorFlow 1] [PyTorch]
- Multilayer Perceptron [TensorFlow 1] [PyTorch]
- Multilayer Perceptron with Dropout [TensorFlow 1] [PyTorch]
- Multilayer Perceptron with Batch Normalization [TensorFlow 1] [PyTorch]
- Multilayer Perceptron with Backpropagation from Scratch [TensorFlow 1] [PyTorch]
- Convolutional Neural Network [TensorFlow 1] [PyTorch]
- Convolutional Neural Network with He Initialization [PyTorch]
- Replacing Fully-Connnected by Equivalent Convolutional Layers [PyTorch]
- All-Convolutional Neural Network [PyTorch]
- AlexNet on CIFAR-10 [PyTorch]
- Convolutional Neural Network VGG-16 [TensorFlow 1] [PyTorch]
- VGG-16 Gender Classifier Trained on CelebA [PyTorch]
- Convolutional Neural Network VGG-19 [PyTorch]
- ResNet and Residual Blocks [PyTorch]
- ResNet-18 Digit Classifier Trained on MNIST [PyTorch]
- ResNet-18 Gender Classifier Trained on CelebA [PyTorch]
- ResNet-34 Digit Classifier Trained on MNIST [PyTorch]
- ResNet-34 Gender Classifier Trained on CelebA [PyTorch]
- ResNet-50 Digit Classifier Trained on MNIST [PyTorch]
- ResNet-50 Gender Classifier Trained on CelebA [PyTorch]
- ResNet-101 Gender Classifier Trained on CelebA [PyTorch]
- ResNet-152 Gender Classifier Trained on CelebA [PyTorch]
- Network in Network CIFAR-10 Classifier [PyTorch]
- Siamese Network with Multilayer Perceptrons [TensorFlow 1]
- Autoencoder [TensorFlow 1] [PyTorch]
- Convolutional Autoencoder with Deconvolutions / Transposed Convolutions[TensorFlow 1] [PyTorch]
- Convolutional Autoencoder with Deconvolutions (without pooling operations) [PyTorch]
- Convolutional Autoencoder with Nearest-neighbor Interpolation [TensorFlow 1] [PyTorch]
- Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on CelebA [PyTorch]
- Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on Quickdraw [PyTorch]
- Conditional Variational Autoencoder (with labels in reconstruction loss) [PyTorch]
- Conditional Variational Autoencoder (without labels in reconstruction loss) [PyTorch]
- Convolutional Conditional Variational Autoencoder (with labels in reconstruction loss) [PyTorch]
- Convolutional Conditional Variational Autoencoder (without labels in reconstruction loss) [PyTorch]
- Fully Connected GAN on MNIST [TensorFlow 1] [PyTorch]
- Convolutional GAN on MNIST [TensorFlow 1] [PyTorch]
- Convolutional GAN on MNIST with Label Smoothing [PyTorch]
- A simple single-layer RNN (IMDB) [PyTorch]
- A simple single-layer RNN with packed sequences to ignore padding characters (IMDB) [PyTorch]
- RNN with LSTM cells (IMDB) [PyTorch]
- RNN with LSTM cells and Own Dataset in CSV Format (IMDB) [PyTorch]
- RNN with GRU cells (IMDB) [PyTorch]
- Multilayer bi-directional RNN (IMDB) [PyTorch]
- A simple character RNN to generate new text (Charles Dickens) [PyTorch]
- Ordinal Regression CNN -- CORAL w. ResNet34 on AFAD-Lite [PyTorch]
- Ordinal Regression CNN -- Niu et al. 2016 w. ResNet34 on AFAD-Lite [PyTorch]
- Ordinal Regression CNN -- Beckham and Pal 2016 w. ResNet34 on AFAD-Lite [PyTorch]
- Cyclical Learning Rate [PyTorch]
- Using PyTorch Dataset Loading Utilities for Custom Datasets -- CSV files converted to HDF5 [PyTorch]
- Using PyTorch Dataset Loading Utilities for Custom Datasets -- Face Images from CelebA [PyTorch]
- Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from Quickdraw [PyTorch]
- Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from the Street View House Number (SVHN) Dataset [PyTorch]
- Dataloading with Pinned Memory [PyTorch]
- Standardizing Images [PyTorch]
- Image Transformation Examples [PyTorch]
- Char-RNN with Own Text File [PyTorch]
- Sentiment Classification RNN with Own CSV File [PyTorch]
- Using Multiple GPUs with DataParallel -- VGG-16 Gender Classifier on CelebA [PyTorch]
- Sequential API and hooks [PyTorch]
- Weight Sharing Within a Layer [PyTorch]
- Plotting Live Training Performance in Jupyter Notebooks with just Matplotlib [PyTorch]
- Getting Gradients of an Intermediate Variable in PyTorch [PyTorch]
- Chunking an Image Dataset for Minibatch Training using NumPy NPZ Archives [TensorFlow 1]
- Storing an Image Dataset for Minibatch Training using HDF5 [TensorFlow 1]
- Using Input Pipelines to Read Data from TFRecords Files [TensorFlow 1]
- Using Queue Runners to Feed Images Directly from Disk [TensorFlow 1]
- Using TensorFlow's Dataset API [TensorFlow 1]
- Saving and Loading Trained Models -- from TensorFlow Checkpoint Files and NumPy NPZ Archives [TensorFlow 1]