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rohitg00/ai-engineering-from-scratch
Learn it. Build it. Ship it for others.
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Explore 50 GitHub repositories focused on deep-learning. Discover top-starred projects and those trending this week.
Learn it. Build it. Ship it for others.
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.
This repository is a curated collection of hands-on data science projects tailored for beginners. Whether you're just starting your journey in data science or looking to strengthen your skills, these projects provide a practical and interactive way to apply your knowledge.
An Open Source Machine Learning Framework for Everyone
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Open Source Computer Vision Library
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
12 Weeks, 24 Lessons, AI for All!
We write your reusable computer vision tools. 💜
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Machine Learning Systems: Foundations, Scaling, Agentic AI, and Physical AI (Vols I–IV) • Harvard CS249r | https://mlsysbook.ai
An Open Source Machine Learning Framework for Everyone
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Open Source Computer Vision Library
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
12 Weeks, 24 Lessons, AI for All!
Learn it. Build it. Ship it for others.
We write your reusable computer vision tools. 💜
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Machine Learning Systems: Foundations, Scaling, Agentic AI, and Physical AI (Vols I–IV) • Harvard CS249r | https://mlsysbook.ai
JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
High-performance In-browser LLM Inference Engine
🐫 CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents. https://www.camel-ai.org
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
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.
The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.
YuE2: frontier music generation with symbolic planning, zero-shot covers, and agentic music editing.
so-vits-svc fork with realtime support, improved interface and more features.
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.
推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/
StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models
mlpack: a fast, header-only C++ machine learning library