Tutorials

Free YouTube series building neural networks from scratch in Python, from micrograd to GPT-2. The most recommended LLM fundamentals course.

A comprehensive course on computer vision fundamentals, covering image processing, object detection, and deep learning applications.

A comprehensive course series that teaches the foundations of deep learning, how to build neural networks, and how to lead machine learning projects.

A comprehensive course on deep learning with PyTorch. This tutorial series covers everything from the basics to advanced topics in neural networks, computer vision, and natural language processing.

Collection of 1-2 hour practical AI courses taught by industry leaders (Andrew Ng, Harrison Chase, etc.) covering LLMs, RAG, agents, fine-tuning, and more.

Top-down, practical deep learning course by Jeremy Howard. Covers vision, NLP, tabular data, and diffusion models with minimal math prerequisites.

A hands-on course covering the fundamentals of how generative AI works, and how to deploy LLMs responsibly.

Free course covering Transformers, tokenizers, fine-tuning, and the entire HuggingFace ecosystem. Official and kept up to date.

Practical course on building LLM-powered applications covering prompting, RAG, fine-tuning, evaluation, and deployment.

Stanford Machine Learning course by Andrew Ng covering supervised learning, unsupervised learning, and best practices for applying ML to robotics and AI applications.

A comprehensive course by Google that covers machine learning concepts, featuring interactive visualizations and real-world examples.

Official getting started guide for implementing Model Context Protocol servers and clients from Anthropic.

Anthropic official announcement introducing the Model Context Protocol (MCP), an open standard for connecting AI assistants to external data sources.

A free course designed for people with coding experience who want to learn how to apply deep learning and machine learning to practical problems.

Official PyTorch tutorials covering everything from basics to advanced topics in deep learning, with hands-on examples and code.

Educational resource designed to help anyone learn deep reinforcement learning. Provides clean implementations of key algorithms, educational exercises, documentation, and tutorials.

Stanford's flagship NLP course covering word vectors, RNNs, Transformers, LLMs, and modern NLP techniques. Free lecture videos and assignments available online.

Stanford's computer vision course covering CNNs, object detection, segmentation, and vision-language models. Lecture notes and assignments freely available.