Local UI to run and train LLMs and diffusion models. 8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more.
Global
#1793 · +14 · 1d ·GitHub
8-27B bf16 (130 tok/s), 262k context, prefix caching. Works with Claude Code, Codex, opencode and pi.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
#3175 · +5 · 1d ·GitHub
Generate audiobooks from e-books, voice cloning & 1158+ languages!
#3545 · +4 · 1d ·GitHub
machine learning and deep learning tutorials, articles and other resources
13 and is available on Linux, MacOS, and Windows.
#4541 · +2 · 1d ·GitHub
深度学习入门教程, 优秀文章, Deep Learning Tutorial
#5186 · +1 · 1d ·GitHub
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
#5284 · +1 · 1d ·GitHub
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
#5342 · +1 · 1d ·GitHub
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
#5767 · +1 · 1d ·GitHub
A docker image containing the code and the dependencies is available on DockerHub. 03):
#6412 · +0 · 1d ·GitHub
This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer.
#6606 · +0 · 1d ·GitHub
We recommend running these notebooks on Colab, which provides a hosted runtime with all the dependencies you will need. You can also, run these notebooks locally, either by setting up your own Jupyter environment, or using Colab's instructions for running locally.