TranslatedPython - 100 Days from Novice to Master
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21 Lessons, Get Started Building with Generative AI
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
- ml
- data-science
- machine-learning
- machine-learning-algorithms
18 Lessons to Get Started Building AI Agents
- agentic-ai
- agentic-framework
- agentic-rag
- ai-agents
Examples and guides for using the OpenAI API
A latent text-to-image diffusion model
12 Weeks, 24 Lessons, AI for All!
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.
A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.
Python Data Science Handbook: full text in Jupyter Notebooks
Learn how to develop, deploy and iterate on production-grade ML applications.
Data Engineering Zoomcamp is a free 9-week course on building production-ready data pipelines. Join the course here 👇🏼
This is a repo with links to everything you'd ever want to learn about data engineering
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
- tensorflow
- tutorial
- examples
- deep-learning
AirLLM 2.8T LLM inference with single 4GB GPU
- chinese-nlp
- finetune
- generative-ai
- instruct-gpt
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
Google Research
Anthropic's Interactive Prompt Engineering Tutorial
10 Weeks, 20 Lessons, Data Science for All!
Translated📚 Building large models from scratch
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Translated"开源大模型食用指南" (Open-Source Large Model Usage Guide): a tutorial tailor-made for Chinese users on quickly fine-tuning (full-parameter/LoRA) and deploying domestic and international open-source large language models (LLM) and multimodal large models (MLLM) in a Linux environment
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.