100 Days of ML Coding
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We write your reusable computer vision tools. 💜
500 AI Machine learning Deep learning Computer vision NLP Projects with code
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
CVPR 2026 论文和开源项目合集
Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.
100-Days-Of-ML-Code中文版
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
pix2tex: Using a ViT to convert images of equations into LaTeX code.
A neural network that transforms a design mock-up into a static website.
This repository is no longer maintained.
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
✍🏻 Source Code Deep Dives, System Design & Engineering Blogs | Halfrost-Field 冰霜之地:源码解析、系统设计与工程实践笔记
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
The "Python Machine Learning (1st edition)" book code repository and info resource
cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集,极市团队整理
Low-code framework for building custom LLMs, neural networks, and other AI models
Fast and Accurate ML in 3 Lines of Code
Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
Efficiently computes derivatives of NumPy code.
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.