Stars
A complete computer science study plan to become a software engineer.
Repo for counting stars and contributing. Press F to pay respect to glorious developers.
Curated list of project-based tutorials
Virtual whiteboard for sketching hand-drawn like diagrams
Tensors and Dynamic neural networks in Python with strong GPU acceleration
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
Making large AI models cheaper, faster and more accessible
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
An interactive git visualization and tutorial. Aspiring students of git can use this app to educate and challenge themselves towards mastery of git!
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
A playbook for systematically maximizing the performance of deep learning models.
Oh my tmux! My self-contained, pretty & versatile tmux configuration made with 💛🩷💙🖤❤️🤍
Fast and memory-efficient exact attention
wangEditor, open-source Web rich text editor 开源 Web 富文本编辑器
Development repository for the Triton language and compiler
tiktoken is a fast BPE tokeniser for use with OpenAI's models.
A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Ongoing research training transformer models at scale
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"