Tensors and Dynamic neural networks in Python with strong GPU acceleration

Python Data Science Handbook: full text in Jupyter Notebooks

30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

The fundamental package for scientific computing with Python.

🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

Essential Cheat Sheets for deep learning and machine learning researchers https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5

The Unified AI Framework

Parallel computing with task scheduling

机器学习相关教程

100 numpy exercises (with solutions)

阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构

An open access book on scientific visualization using python and matplotlib

Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course

Open Machine Learning Course

NumPy aware dynamic Python compiler using LLVM

Trax — Deep Learning with Clear Code and Speed

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

Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

NumPy & SciPy for GPU