ABOUT
HOW IT WORKS
FOLLOW US
FEEDBACK
Discover useful tools, tutorials, and helpful resources organized by category
CS224N is Stanford's course on natural language processing with deep learning, taught in Winter 2026 by Diyi Yang and Yejin Choi. It starts with word vectors, neural networks and backpropagation, then moves through language models, transformers and self-attention, pretraining, post-training, prompting and parameter-efficient fine-tuning. The current schedule also covers agents, tool use and retrieval-augmented generation, benchmarking and evaluation, reasoning and interpretability. Slides, notes and assignments are posted on the course site, and lecture videos from the 2024 offering are free on YouTube. A solid path for developers who know Python and basic machine learning and want a structured route from embeddings to modern LLM practice.
CS336 is a Stanford course taught by Tatsunori Hashimoto and Percy Liang in which students build their own language model end to end. It covers the entire development process: collecting and cleaning pre-training data, constructing the transformer, training the model, and evaluating it before deployment. The lecture videos are public on YouTube and the course materials are on the course site. One of the most thorough free paths for engineers who want to understand LLMs by building one rather than only calling an API.
Prompting Guide is a comprehensive online resource created by DAIR.AI that gathers up-to-date theory and best practices for designing effective prompts when working with large language models (LLMs). It covers the basics of prompting—such as instructions, context, input data, and output formats—while providing more advanced techniques like function calling, context caching, synthetic dataset generation, and handling model risk (bias, factuality, etc.). Users can find example prompts (for tasks like summarization, reasoning, QA, coding), model-specific guidance, tools, notebooks, and research readings, all intended to help developers, researchers, and AI practitioners get better results and understand LLM capabilities more deeply.
Grant Sanderson’s (3Blue1Brown) Neural Networks playlist offers an elegant and visual exploration of how neural networks learn—from the basics of node-to-node processing and gradient descent to backpropagation and feature learning—using rich animations and intuitive storytelling that make complex concepts feel both beautiful and approachable.
This free YouTube playlist compiles the latest edition of Stanford’s legendary CS 231N: Deep Learning for Computer Vision course, presenting a thorough, lecture-by-lecture exploration of how neural networks interpret and generate images. From core concepts like convolutional layers and optimization techniques to modern techniques in object detection, generative modeling, and explainability, the course is tailored for both students and professionals looking to deepen their understanding of cutting-edge computer vision methods.
A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.
DeepLearning.AI is a pioneering education technology company founded by AI innovator Andrew Ng in 2017. Its mission is to equip a global audience—from aspiring AI professionals to non-technical learners—with high-quality, hands-on training in artificial intelligence. Through widely respected programs on Coursera like the Deep Learning Specialization and AI for Everyone, DeepLearning.AI has helped millions build tangible machine learning skills, supported by a collaborative community and a growing ecosystem of AI tools and partnerships.