Tensors and Dynamic neural networks in Python with strong GPU acceleration
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The 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 than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
Python Data Science Handbook: full text in Jupyter Notebooks
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 AI models with fast, easy-to-use and efficient data manipulation tools
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
A high-performance, zero-overhead, extensible Python compiler with built-in NumPy support
100 numpy exercises (with solutions)
Convert Machine Learning Code Between Frameworks
Parallel computing with task scheduling
人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域
机器学习相关教程
NumPy & SciPy for GPU
An open access book on scientific visualization using python and matplotlib
NumPy aware dynamic Python compiler using LLVM
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
Open Machine Learning Course
Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
tensorboard for pytorch (and chainer, mxnet, numpy, ...)
Efficiently computes derivatives of NumPy code.
Performance analysis of predictive (alpha) stock factors
Fast data visualization and GUI tools for scientific / engineering applications
N-D labeled arrays and datasets in Python