Tensors and Dynamic neural networks in Python with strong GPU acceleration

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

The fundamental package for scientific computing with Python.

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

A high-performance, zero-overhead, extensible Python compiler with built-in NumPy support

Convert Machine Learning Code Between Frameworks

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

Parallel computing with task scheduling

100 numpy exercises (with solutions)

机器学习相关教程

An open access book on scientific visualization using python and matplotlib

NumPy aware dynamic Python compiler using LLVM

NumPy & SciPy for GPU

Open Machine Learning Course

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

Trax — Deep Learning with Clear Code and Speed

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

Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy

A flexible framework of neural networks for deep learning