A course by Andrej Karpathy on building neural networks, from scratch, in code. We start with the basics of backpropagation and build up to modern deep neural networks, like GPT. In my opinion language models are an excellent place to learn deep learning, even if your intention is to eventually go to other areas like computer vision because most of what you learn will be immediately transferable. This is why we dive into and focus on languade models.

Introduction to RL with intuitive explanations and fun examples, taught by one of the world’s leading RL experts.

Taught by one of the most influential (and most down-to-earth) researcher, Christopher Manning, this is must-take course for anyone interested in natural language processing. The course is well organized, well taught, and up-to-date with the latest NLP research.

Learn the basics of neural networks and backpropagation, one of the most important algorithms for the modern world.

A free course offering the core concept of linear algebra with a visuals-first approach.

A free course offering the core concept of Calculus, with a visuals-first approach aimed at making you feel like you could have discovered the subject yourself.

Everything you need to know, from the basics, to image classification with Convolutional Neural Networks, to the state of the art transformers used for large language models like ChatGPT, presented one step at a time so that it is easily understood.

A gentle introduction to Machine Learning. Linear Models and Logistic Regression are just the tips of the machine learning iceberg. There’s tons more to learn, and this playlist will help you trough it all, one step at a time.