Explore Projects
Discover 425 open source projects
Showing 1-20 of 425 projects
mlabonne/llm-course
Comprehensive LLM course with roadmaps, Colab notebooks, and tools for building and deploying LLM applications.
d2l-ai/d2l-zh
深度学习教学资源，包含可运行代码和讨论论坛。
dair-ai/Prompt-Engineering-Guide
Comprehensive guide to prompt engineering, RAG, and AI agents for developers
facebookresearch/segment-anything
Segment Anything Model for image/video segmentation with AI prompts
jakevdp/PythonDataScienceHandbook
Python Data Science Handbook in Jupyter Notebooks
gradio-app/gradio
Gradio App for building and sharing delightful machine learning apps
TriliumNext/Trilium
Hierarchical note-taking app for building personal knowledge bases
ageron/handson-ml2
Hands-on Machine Learning with Python, Scikit-Learn, Keras, and TensorFlow 2.
donnemartin/data-science-ipython-notebooks
Data science Python notebooks covering deep learning, machine learning, big data, and more.
CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
Bayesian methods and probabilistic programming in Python with PyMC
d2l-ai/d2l-en
Interactive deep learning book with code and math
fastai/fastai
Deep learning library for fast and easy model building
ageron/handson-ml
Deprecated ML book repo with Jupyter notebooks for learning fundamentals
aishwaryanr/awesome-generative-ai-guide
Comprehensive resource for generative AI research, interviews, and courses
fastai/fastbook
Deep learning book with Jupyter Notebooks covering fastai and PyTorch
wesm/pydata-book
Companion repo for Python for Data Analysis book with Jupyter notebooks and data science examples
trekhleb/homemade-machine-learning
Interactive machine learning algorithms in Python with Jupyter demos
HandsOnLLM/Hands-On-Large-Language-Models
Official code repo for the O'Reilly book on LLMs with Jupyter Notebooks
MLEveryday/100-Days-Of-ML-Code
机器学习100天学习计划，涵盖有监督和无监督学习算法
recommenders-team/recommenders
Recommenders is a project for prototyping and operationalizing recommendation systems with Jupyter notebooks and best practices.
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