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A comparison of deep-learning frameworks  
A comparison of deep-learning frameworks (from Facebook)  
Autoencoder network for learning a continuous representation of molecular structures.  
CGT   - Computation Graph Toolkit (CGT) is a library for evaluation and differentiation of functions of multidimensional arrays.
CleverHans   - A library for benchmarking vulnerability to adversarial examples.
Deep learning frameworks: Overview  
Deep learning galery  
Deep learning software  
Distributed (Deep) Machine Learning Community   - a collection of interesting ML projects with a focus on distributed computation
Geometric deep learning  
gnumpy   - a simple Python module that interfaces in a way almost identical to numpy, but does its computations on your computer's GPU.
horovod   - Distributed training framework for TensorFlow (claimed to be faster than traditional Distributed TensorFlow).
Hummingbird   - a library for compiling trained traditional ML models into tensor computations. Hummingbird allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models.
Keras.JS   - Run trained Keras models in your browser, GPU-powered using WebGL. Models are serialized directly from the Keras JSON-format configuration file and associated HDF5 weights.
NNPACK   - Acceleration package for neural networks on multi-core CPUs
OverFeat   - an image recognizer and feature extractor built around a convolutional network.
Rapids   - a suite of open source software libraries allowing one to execute end-to-end data science and analytics pipelines entirely on GPUs.
SqueezeNet   - AlexNet-level accuracy with 50x fewer parameters
TorchCraft   - an interface between StarCraft: Brood War and Torch, the deep learning environment.