All free AI courses
MIT 6.S191ExpertFree
MIT Introduction to Deep Learning (2026)
MIT’s fast-paced deep learning bootcamp, refreshed every year. Covers sequence models and transformers, computer vision, generative models, reinforcement learning and current frontiers.
9 video lessons · 8 h 19 min · taught by Alexander Amini, Ava Amini
Lesson 1 of 9 · 56 min
Introduction to Deep Learning
Check your understanding
Course quiz
Finish the course to unlock the quiz
Mark all 9 lessons as watched and 8 questions open up here. You have 9 to go.
What you will learn
- Explain perceptrons, loss functions and training by gradient descent
- Compare RNNs, attention and transformers for sequence data
- Describe how CNNs, VAEs, GANs and diffusion models work
- Frame a problem as reinforcement learning with states, actions and rewards
Before you start
- Prerequisites
- Linear algebra and calculus (matrix multiplication, derivatives, the chain rule). Python helps.
- Cost
- Free. Lectures, slides and software labs are open to everyone.
Videos are embedded from MIT 6.S191’s own channel and remain theirs. The quiz is written by MTT to help you check your understanding; it is not an official MIT 6.S191 assessment.