Tensors and Dynamic neural networks in Python with strong GPU acceleration

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.

tensorboard for pytorch (and chainer, mxnet, numpy, ...)

Code examples in pyTorch and Tensorflow for CS230

PyTorch extensions for high performance and large scale training.

Code for the paper "PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications"

A language-agnostic interface to TensorBoard

Train NLTK objects with zero code

Hybrid CPU/GPU implementation of the A3C algorithm for deep reinforcement learning.

TensorFlow implementation of Neural Variational Inference for Text Processing

Tutorial for building a custom CUDA function for Pytorch

Process Common Crawl data with Python and Spark

End-to-End Attention-Based Large Vocabulary Speech Recognition

Virtual adversarial training with Tensorflow

Tools for PyTorch

Variational and semi-supervised neural network toppings for Lasagne

Implementation of the Convolution Neural Network for factoid QA on the answer sentence selection task

Good Semi-Supervised Learning That Requires a Bad GAN

Code for NLTK3 Cookbook

Deep generative models for semi-supervised learning.

Weight initialization schemes for PyTorch nn.Modules

Reference implementation for "Neural Sequence Model Training via α-divergence Minimization"