Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Python Data Science Handbook: full text in Jupyter Notebooks
Build your personal knowledge base with Trilium Notes
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
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.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
The fastai book, published as Jupyter Notebooks
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
A one stop repository for generative AI research updates, interview resources, notebooks and much more!
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
Transform data, train models, and run SQL with marimo — feels like a next-gen reactive notebook, stored as Git-friendly reproducible Python. Deploy as scripts, pipelines, endpoints, and apps. All from an AI-native editor (or your own).
✔（已完结）最全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】
🥗 All-in-one professional pop-up dictionary and page translator which supports multiple search modes, page translations, new word notebook and PDF selection searching.
A fully open source & end-to-end encrypted note taking alternative to Evernote.
Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI
Free ways to dive into machine learning with Python and Jupyter Notebook. Notebooks, courses, and other links. (First posted in 2016.)
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
:notebook_with_decorative_cover: :books: A curated list of awesome resources : books, videos, articles about using Next.js (A minimalistic framework for universal server-rendered React applications)
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like YOLO11, RT-DETR, SAM 2, Florence-2, PaliGemma 2, and Qwen2.5VL.
Ready-to-run Docker images containing Jupyter applications
Multi-user server for Jupyter notebooks
Notebooks and code for the book "Introduction to Machine Learning with Python"
fast-stable-diffusion + DreamBooth
:ant:前端面试复习笔记
CLI and local web plain text note‑taking, bookmarking, and archiving with linking, tagging, filtering, search, Git versioning & syncing, Pandoc conversion, + more, in a single portable script.
A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)