machine-learning
Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
Repositories (1435)
Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.
ModelScope: bring the notion of Model-as-a-Service to life.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learnin...
A Beautiful Open Source RSS & Podcast App Powered by Getstream.io
A curated list of awesome embedded programming.
Deep universal probabilistic programming with Python and PyTorch
Learning Convolutional Neural Networks with Interactive Visualization.
The backtesting engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.
Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
NSFW detection on the client-side via TensorFlow.js
Interview = 简历指南 + 算法题 + 八股文 + 源码分析
A collection of machine learning examples and tutorials.
深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）
A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!
🧙 Build, run, and manage data pipelines for integrating and transforming data.
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reduction...
TensorFlow tutorials and best practices.
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
A resource for learning about Machine learning & Deep Learning
Build your neural network easy and fast, 莫烦Python中文教学
Accessible large language models via k-bit quantization for PyTorch.
High-performance AI pipeline engine with a C++ core and 50+ Python-extensible nodes. Build, debug, and scale LLM workflows with 13+ model providers, 8...
WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.
Caffe2 is a lightweight, modular, and scalable deep learning framework.
A collection of libraries to optimise AI model performances
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language...
StarCraft II Learning Environment
Trax — Deep Learning with Clear Code and Speed
BoxMOT: Pluggable Python and C++ SOTA multi-object tracking modules with support for axis-aligned and oriented bounding boxes
Uniform Manifold Approximation and Projection
BertViz: Visualize Attention in Transformer Models
告别枯燥，致力于打造 Python 实用小例子，更多Python良心教程见 https://ai-jupyter.com
A self-hosted open source photo management service.
Production infrastructure for machine learning at scale
tensorboard for pytorch (and chainer, mxnet, numpy, ...)
A Smart, Automatic, Fast and Lightweight Web Scraper for Python
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular dat...
⚡ TabPFN: Foundation Model for Tabular Data ⚡
A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
Machine Learning Yearning 中文版 - 《机器学习训练秘籍》 - Andrew Ng 著
22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
Flexible and powerful framework for managing multiple AI agents and handling complex conversations
Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现
A 2.78-trillion-parameter Kimi K3 running inference on a single CPU in 8.24 GB of RAM. Portable C99: no BLAS, no framework, no GPU.
Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.