计算机学科基础知识和主流编程语言相关内容的总结

Official JAX implementation of Learning to (Learn at Test Time): RNNs with Expressive Hidden States

LLM101n: Let's build a Storyteller

Stochastic Optimization under Uncertainty in Python.

软件工程师、算法工程师、机器学习工程师、数据科学家实践与面试

Public repository for Boffi & Vanden-Eijnden, PNAS (2024).

Transfer learning on PINNs for tracking hemodynamics

Get up and running with Llama 3, Mistral, Gemma 2, and other large language models.

🤖 The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs gguf, transformer…

Universal LLM Deployment Engine with ML Compilation

🥢像老乡鸡🐔那样做饭。文字来自《老乡鸡菜品溯源报告》，并做归纳、编辑与整理。CookLikeHOC.

The official Meta Llama 3 GitHub site

Differentiable Finite Element Method with JAX

Neural Networks: Zero to Hero

LLM training in simple, raw C/CUDA

Boosting Latent Diffusion with Flow Matching

Normalizing-flow enhanced sampling package for probabilistic inference in Jax

Differentiable, Hardware Accelerated, Molecular Dynamics

code for "Generalized Schrödinger Bridge Matching" (ICLR 2024).

Bayesian Optimization and Design of Experiments

18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

Light and Optimal Schrödinger Bridge Matching official PyTorch implementation

Learning Algorithm

📚 AIGC 求职面经、必备基础知识、提示词工程、ChatGPT、Stable Diffusion、Prompt、Embedding、Fintune 等 AIGC 求职你所需要知道的一切~