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CUTLASS is a collection of abstractions for implementing high-performance matrix-matrix multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement.

This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."

We are in the process of writing a book on Mathematics for Machine Learning that motivates people to learn mathematical concepts. The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this.

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

SciPy (pronounced "Sigh Pie") is an open-source software for mathematics, science, and engineering. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more.

Implemented the K-NN algorithm for classification. #100DaysOfMLCode Support Vector Machine Infographic is halfway complete. Will update it tomorrow.

This is aimed at promoting understanding of vector/matrix calculations and algorithms from the perspectives of matrix factorizations.

DeepGEMM-Ascend: clean and efficient matrix multiplication kernel library for Huawei Ascend NPUs

Book4《矩阵力量》 | 鸢尾花书：从加减乘除到机器学习；上架！

Algorithms and data structures are fundamental to efficient code and good software design. Creating and designing excellent algorithms is required for being an exemplary programmer.

This course is focused on the question: How do we do matrix computations with acceptable speed and acceptable accuracy?

until forever. Exceptional understanding of French with the flawless accent implies correct exposure to exceptional French and flawless accent. Similarly, great AI Researchers & engineers with excellent problem-skills imply quality knowledge consumed and exposure rich experience.

100-Days-Of-ML-Code中文版

Am I missing your favorite model? Is there something that could be cleaner / less confusing? Did I mess something up?

This is the most comprehensive book available on the deep learning and available as free html book for reading at

Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library.