- Deep Learningby UC Berkeley
- Neural networks by 3blue1brown
- Stanford CS224N Natural Language
- Building Neural Networks from Scratch
- Deep Generative Models
- Stanford CS25 - Transformers United
- Deep Generative Models
- CIS 522 - Deep Learning
- Building LLMs from scratch
- Stanford CS224N: Natural Language Processing
- Stanford CS236: Deep Generative Models
- Neural Networks Demystified
- Applied Machine Learning
- Deep Learning by Code Emporium
- Full Stack Deep Learning - Spring
- Stanford XCS224U: Natural Language Processing
- Deep Learning by Josh Starmer
- Deep Learning With PyTorch - Full Course
- Deep Learning Architectures by Yannic Kilcher
- Stanford CS224N: Natural Language Processing with Deep Learning
- CS231n by Andrej Karpathy
- Selected topics in Deep Learning by DeepBean
- MIT 6.801 Machine Vision
- Deep Learning by Prof Bryce
- Convolutional Neural Networks
- Meta Learning: Deep Learning Guide
- Introduction to Deep Learning by MIT
- LLM Bootcamp
- Backpropagation by Prof Bryce
- Deep Learning by Jeremy Howard
- CS229 Lecture Notes by Andrew Ng and Tengyu Ma
- Speech and Language Processing
- Natural Language Processing by Yannic Kilcher
- Stanford CS224W Machine Learning with Graphs
- Deep Learning and Generative Models
- Full Stack Deep Learning
- Stanford CS224U: Natural Language Processing
- Guest Lecture Series in Large Language Models
- Hugging Face Course
- Courses from Deep Learning AI
- Deep Learning
- Full Stack Deep Learning
- Modern computer vision
- The Complete Mathematics of Neural Networks and Deep Learning by Adam Dhalla
- Deep Learning for Computer Vision by IIT Madras
- Deep Learning Book Companion Videos
- Deep Learning by Ahlad Kumar
- Group Equivariant Deep Learning
- Machine Learning by Erik Bekkers
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
- Foundations of Large Language Models
- What Is ChatGPT Doing … and Why Does It Work?
- The little book of deep learning
- The Illustrated Stable Diffusion
- Introduction to Deep Learning by Sebastian Raschka
- Foundations of Machine Learning
- The Annotated Transformer
- Deep Learning
- Understanding Deep Learning
- Prompt Engineering Guide
- The Scaling Hypothesis
- Building LLM applications for production
- Neural networks by Michael Nielsen
- How Large Language Models work
- Overview of Large Language Models
- Brex's Prompt Engineering Guide
- Approaching (Almost) Any Machine Learning Problem
- The Illustrated Transformer
- Understanding LLMs from Scratch using Middle School Math
- Neural Networks: Zero to Hero
- Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
- Machine Learning and Deep Learning by Sebastian Raschka
- Tensorflow tutorials
- Tutorial and Workshop Recordings for LLMs
- Papers with code
- Attention in Transformers: Concepts and Code in PyTorch
- How Transformer LLMs Work
- Pytorch tutorials by Aladdin Persson
- TensorFlow 2.0 Complete Course
- Tutorials from freeCodeCamp.org
- Machine Learning in C
- Learn PyTorch for Deep Learning: Zero to Mastery book
- Large Language Models (LLMs)
- PyTorch Tutorials
- Deep Learning With Tensorflow 2.0
- Recurrent Neural Networks
- Code LLMs
- Smol course
- Machine Learning Notebook
- Transformers Notebooks
- Create a Large Language Model from Scratch with Python
- Learn PyTorch for deep learning in a day
- Cookbook for advanced deep learning
- Machine Learning and Statistics
- Machine Learning by Josh Starmer
- Applied Machine Learning (Cornell Tech)
- Stanford CS229: Machine Learning
- Visual explanations of core machine learning concepts
- Building Decision Trees from Scratch
- Stanford EE104: Introduction to Machine Learning
- Complete ML course in 60 Hours
- CORNELL CS4780 Machine Learning
- Machine Learning
- Stanford CS221: Artificial Intelligence
- Stanford CS229M: Machine Learning
- Pattern Recognition
- CS50's Introduction to Artificial Intelligence with Python
- MIT 6.034 Artificial Intelligence
- Machine Learning by IIT Madras
- General Machine Learning by Yannic Kilcher
- Bayes' Classifier by Pratik Jain
- Pattern Recognition by Pratik Jain
- Deep Reinforcement Learning UC Berkeley 2023
- Deep Reinforcement Learning UC Berkeley 2021
- Stanford CS234 Reinforcement Learning
- Reinforcement Learning by Steve Brunton
- Deep Reinforcement Learning Course
- Artificial Intelligence Search Methods For Problem Solving
- Deep Reinforcement Learning UC Berkeley 2020
- Reinforcement Learning by Yannic Kilcher
- Reinforcement Learning 101
- Stanford CS234: Reinforcement Learning
- Deep Reinforcement Learning RAIL
- Spinning Up by OpenAI
- RLHF: Reinforcement Learning from Human Feedback
- Algorithms for Reinforcement Learning
- Reinforcement Learning by Richard S. Sutton and Andrew G. Barto
- Calculus Visualized by Dennis F Davis
- Foundations for Machine Learning
- MIT 18.S096 Matrix Calculus For Machine Learning And Beyond
- Optimization Theory and Algorithms
- Essence of calculus by 3blue1brown
- The Matrix Calculus You Need For Deep Learning
- Matrix Calculus
- Vector Calculus
- Mathematics for Machine Learning
- Vector Calculus
- Differential and Integral Calculus
- Algorithms for Optimization
- Probability Theory for Data Science
- MIT RES.6-012 Introduction to Probability
- Seeing Probability
- Probability Foundation
- Probability Bootcamp
- MIT 6.041SC Probabilistic Systems Analysis
- Probability and Random Variables
- Introduction to probability and Statistics
- Stanford CS109 Introduction to Probability for Data Science
- Linear Algebra and Matrix Theory by Pratik Jain
- Linear Algebra by 3Blue1Brown
- Interactive Linear Algebra
- Applied Linear Algebra
- MIT 18.06 Linear Algebra
- Linear Algebra by Dr. Trefor Bazett
- The Matrix Cookbook
- Matrix Algebra for Engineers
- Matrix Theory
- Linear Algebra done right
- Mathematics for Robotics
- Computational Linear Algebra
- Linear Algebra
- A Vision of Linear Algebra
- Immersive Linear Algebra
- Linear Algebra Done Right book
- MIT 18.06SC Linear Algebra
- MIT 18.065 Matrix Methods
I hope you find this list useful. If you want to join the community, where thousands of learners are mastering AI, join the Discord server.