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🦜🔗 Build context-aware reasoning applications
21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
Python Data Science Handbook: full text in Jupyter Notebooks
Learn how to design, develop, deploy and iterate on production-grade ML applications.
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
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。
📡 Simple and ready-to-use tutorials for TensorFlow
《李宏毅深度学习教程》（李宏毅老师推荐👍，苹果书🍎），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)
A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.
Examples and guides for using the Gemini API
Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI
Lab Materials for MIT 6.S191: Introduction to Deep Learning
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
The "Python Machine Learning (3rd edition)" book code repository
JAX implementation of OpenAI's Whisper model for up to 70x speed-up on TPU.
felixge's notes on the various go profiling methods that are available.
Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
快速上手AI理论及应用实战：基础知识、Transformer、NLP、ML、DL、竞赛。含大量注释及数据集，力求每一位能看懂并复现。
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Datasets, tools, and benchmarks for representation learning of code.
Jupyter notebooks for using & learning Keras
