面向PyTorch/Tensorflow的神经网络图和训练度量的轻量库 https://github.com/waleedka/hiddenlayer pytorch结合友好，画网络图方便直观，可用于notebook实践演示
机器学习DevOps(部署/监控/版本管理与扩展)相关资源大列表 https://github.com/EthicalML/awesome-machine-learning-operations
用于查找数据集中标签错误、用含噪标签进行学习的机器学习包 https://github.com/cgnorthcutt/cleanlab
ONNX模型转换工具，目前已支持Keras, CoreML, LightGBM, Scikit-Learn https://github.com/onnx/onnxmltools
https://github.com/opencv/openvino_training_extensions/tree/develop/pytorch_toolkit/nncf
【Cottonwood 神经网络框架(教学用)】’The Cottonwood Neural Network Framework - A flexible neural network framework for running experiments and trying ideas.'
https://github.com/brohrer/cottonwood
【Thinc：支持PyTorch、TensorFlow、 MXNet等各主流框架的轻量深度学习库，提供优雅的、类型检查的函数式编程 API】 https://thinc.ai/
FAIR Self-Supervised Learning Integrated Multi-modal Environment (SSLIME) https://github.com/facebookresearch/fair-sslime
【基于Scikit-Learn的Python高性能拓扑机器学习工具箱】 https://github.com/giotto-ai/giotto-tda
【DeepSpeed：微软的深度学习优化库，让分布式训练更简单、更高效、更有效】 https://github.com/microsoft/DeepSpeed
Catalyst：聚焦快速开发与复现的深度学习/强化学习高级工具集 https://github.com/catalyst-team/catalyst
【thor：C++深度学习工具库】’thor - thor: C++ helper library, for deep learning purpose' https://github.com/jinfagang/Thor
【Radish：C++深度学习模型训练/部署框架】'Radish - C++ model train&inference framework' https://github.com/LieLuoboai/radish
ImJoy：插件化的深度学习混合计算平台 https://github.com/oeway/ImJoy
BytePS：支持TensorFlow, Keras, PyTorch, MXNet的通用高性能训练框架 https://github.com/bytedance/byteps
Alink：基于Flink的通用算法平台，由阿里巴巴计算平台PAI团队研发 https://github.com/alibaba/Alink
机器学习模型管理框架概览：MLFlow/DVC/Sacred https://www.inovex.de/blog/machine-learning-model-management/
Reaction：快速便捷的深度学习模型服务框架 https://github.com/catalyst-team/reaction
深度学习监控自动化 https://medium.com/nanonets/how-to-automate-surveillance-easily-with-deep-learning-4eb4fa0cd68d
机器学习/AI模型的Panini产品化部署(Panini Beta测试阶段免费) https://towardsdatascience.com/deploy-ml-dl-models-to-production-via-panini-3e0a6e9ef14
ONNC：开放神经网络ONNX编译器 https://github.com/ONNC/onnc
Weight Watcher：深度网络泛化精度预测工具 https://github.com/CalculatedContent/WeightWatcher
神经网络模型可视化浏览器(支持ONNX/Keras/Core ML/TensorFlow Lite等，实验性支持PyTorch/Torch/CNTK/TensorFlow.js/TensorFlow等) https://github.com/lutzroeder/Netron
Xfer：MXNet迁移学习库 https://github.com/amzn/xfer/
【PyTorch项目模板】’pytorch-template - my pytorch project template (for kaggle and research)' by lyakaap https://github.com/lyakaap/pytorch-template
【PyTorch实现的XNOR-Net】’XNOR-Net-PyTorch - PyTorch Implementation of XNOR-Net' by jiecaoyu https://github.com/jiecaoyu/XNOR-Net-PyTorch
用PyTorch训练神经网络的简单工具 https://github.com/belskikh/kekas https://github.com/belskikh/kekas/blob/master/Tutorial.ipynb
Keras Tuner：(Google)以人文本的超参调试框架 https://elie.net/talk/cutting-edge-tensorflow-keras-tuner-hypertuning-for-humans/
Keras超参调试器 https://github.com/keras-team/keras-tuner
studio.ml：用来简化、加快模型构建过程的模型管理框架 https://github.com/studioml/studio