* [ DLIB ](https://github.com/davisking/dlib) \- A toolkit for making real world machine learning and data analysis applications in C++. 
  * [ fastai ](https://github.com/fastai/fastai) \- The fastai library simplifies training fast and accurate neural nets using modern best practices. 
  * [ tpot ](https://github.com/EpistasisLab/tpot) \- A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. 
  * [ deap ](https://github.com/DEAP/deap) \- Distributed Evolutionary Algorithms in Python. 
  * [ gym ](https://github.com/openai/gym) \- A toolkit for developing and comparing reinforcement learning algorithms. 
  * [ tensorflow_ros_cpp ](https://github.com/tradr-project/tensorflow_ros_cpp) \- A ROS package that allows to do Tensorflow inference in C++ without the need to compile TF yourself. 
  * [ Tensorflow Federated ](https://github.com/tensorflow/federated) \- TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data. 
  * [ finn ](https://github.com/Xilinx/finn) \- Fast, Scalable Quantized Neural Network Inference on FPGAs. 
  * [ neuropod ](https://github.com/uber/neuropod) \- Neuropod is a library that provides a uniform interface to run deep learning models from multiple frameworks in C++ and Python. 
  * [ leela-zero ](https://github.com/leela-zero/leela-zero) \- This is a fairly faithful reimplementation of the system described in the Alpha Go Zero paper "Mastering the Game of Go without Human Knowledge". 
  * [ Trax ](https://github.com/google/trax) \- A library for deep learning that focuses on sequence models and reinforcement learning. 
  * [ mlflow ](https://github.com/mlflow/mlflow) \- A platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. 
  * [ Netron ](https://github.com/lutzroeder/Netron) \- Visualizer for neural network, deep learning and machine learning models. 
  * [ MNN ](https://github.com/alibaba/MNN) \- A blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba. 
  * [ Tensorforce ](https://github.com/tensorforce/tensorforce) \- An open-source deep reinforcement learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications in research and practice. 
  * [ Dopamine ](https://github.com/google/dopamine) \- A research framework for fast prototyping of reinforcement learning algorithms. 
  * [ catalyst ](https://github.com/catalyst-team/catalyst) \- Was developed with a focus on reproducibility, fast experimentation and code/ideas reusing. 
  * [ ray ](https://github.com/ray-project/ray) \- A fast and simple framework for building and running distributed applications. 
  * [ tf-agents ](https://github.com/tensorflow/agents) \- A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning. 
  * [ ReAgent ](https://github.com/facebookresearch/ReAgent) \- An open source end-to-end platform for applied reinforcement learning (RL) developed and used at Facebook. 
  * [ Awesome-Mobile-Machine-Learning ](https://github.com/fritzlabs/Awesome-Mobile-Machine-Learning) \- A curated list of awesome mobile machine learning resources for iOS, Android, and edge devices. 
  * [ cnn-explainer ](https://github.com/poloclub/cnn-explainer) \- Learning Convolutional Neural Networks with Interactive Visualization. 
  * [ modelzoo ](https://github.com/autowarefoundation/modelzoo) \- A collection of machine-learned models for use in autonomous driving applications. 
  * [ nnstreamer-ros ](https://github.com/nnstreamer/nnstreamer-ros) \- A set of Gstreamer plugins and ROS examples that allow Gstreamer developers to adopt neural network models easily and efficiently and neural network developers to manage neural network pipelines and their filters easily and efficiently.