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
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.
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
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
Open Machine Learning Course
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
Efficiently computes derivatives of NumPy code.
Fast data visualization and GUI tools for scientific / engineering applications
N-D labeled arrays and datasets in Python
napari: a fast, interactive, multi-dimensional image viewer for python
🎚️ Open Source Audio Matching and Mastering
A python module for scientific analysis of 3D data based on VTK and Numpy
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Python audio and music signal processing library
Python+Numpy+OpenGL: fast, scalable and beautiful scientific visualization