Machine Learning · GitHub

A complete daily plan for studying to become a machine learning engineer.

• "You can, but it is far more difficult than when I got into the field." Drac Smith
• "I'm hiring machine learning experts for my team and your MOOC will not get you the job (there is better news below). In fact, many people with a master's in machine learning will not get the job because they (and most who have taken MOOCs) do not have a deep understanding that will help me solve my problems." Ross C. Taylor

Data science interview questions and answers

Deepfakes Software For All

🚀Clone a voice in 5 seconds to generate arbitrary speech in real-time

stable diffusion webui colab

Making large AI models cheaper, faster and more accessible

• Data Parallelism
• Pipeline Parallelism

Code and documentation to train Stanford's Alpaca models, and generate the data.

The central nervous system and backend orchestrator for Fractal. It handles multi-tenant task scheduling, real-time TFLOPs budgeting, and federated weight aggregation using FedAvg.

• Edge AI & Mobile Compute Orchestration Spotlight: Architectural overview and visual demonstration spotlighting on-device edge intelligence, distributed training rounds, and decentralized compute harvesting across mobile devices.

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+ vector databases, and agent orchestration, all from your IDE. Includes VS Code extension, TypeScript/Python SDKs, and Docker deployment.

• Rocket Builder - Drag, connect, and configure nodes visually, no boilerplate. Real-time observability tracks token usage, LLM calls, latency, and execution. Pipelines are portable JSON, version-controllable, shareable, and runnable anywhere.
• High-Performance C++ Runtime - Native multithreading purpose-built for the throughput demands of AI and data workloads. No bottlenecks, no compromises for production scale.

机器学习、深度学习、自然语言处理、计算机视觉、各种算法等AI领域相关技术的路线、教程、干货分享。笔记有：机器学习实战、剑指Offer、cs231n、cs131、吴恩达机器学习、cs224n、python自然语言处理实战

Self-tuning multi-agent AI trading system. 8-source signal fusion, Bull/Bear/Judge debate on Claude Opus 4.7, Kelly + ATR position sizing. Python · Kalshi + Polymarket adapters.

A simple, high level, easy to use, open source Computer Vision library for Python.

A simple and intuitive library for diffusion models using Flax and Jax. Includes detailed notebooks on DDPM, DDIM, and EDM with simplified mathematical explanations. Made as part of my journey for learning and experimenting with generative AI.

Fast, dependence-aware resampling and uncertainty for time series and ragged panels.

ASTER in Pytorch

[ECCV'20] Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling

Free Course : Data, Machine Learning, Deep Learning and AI Engineering, the long way round: the math, the code, the systems and the security. 173 lessons, 166 runnable labs, no shortcuts. Free under CC BY-NC 4.0.