MIT 6.S191: Introduction to Deep Learning (2026)
by MIT
MIT's deep learning intro, rewritten every year — nine lectures and three Colab labs ending in fine-tuning an LLM.
Overview
6.S191 is MIT's introductory deep learning course, taught by Alexander Amini and Ava Amini, and its distinguishing feature is that the syllabus is rewritten annually rather than archived — the 2026 run spans March 30 to May 25 and its nine lectures are Intro to Deep Learning, Deep Sequence Modeling, Deep Computer Vision, Deep Generative Modeling, Deep Reinforcement Learning, New Frontiers, The Three Laws of AI, AI for Science, and Secrets to Massively Parallel Training. Three software labs carry the practical half: Lab 1 covers deep learning in Python and music generation, Lab 2 builds a facial detection system, and Lab 3 fine-tunes an LLM. Every lab runs in Google Colaboratory against a mitdeeplearning pip package, so there is nothing to install locally beyond a Google account and a GPU runtime. The public lab repository, MITDeepLearning/introtodeeplearning, carries 8.7k stars, 4.6k forks, an MIT license and 736 commits, with material spanning both TensorFlow and PyTorch. Slides and lecture recordings are posted free on the course site and YouTube; registered MIT students take it as a 3-unit course graded pass/D/fail on a project proposal, while external learners get identical material with no certificate. The stated prerequisites are honest and low: matrix multiplication, derivatives and the chain rule, with Python familiarity a plus rather than a requirement.
At a Glance
- Topic
- ML
- Level
- Beginner
- Format
- Course
- Cost
- Free
- Duration
- 9 lectures (~1 hour each) plus 3 Colab labs; ~8 weeks at 3-5 h/week, or self-paced
- Provider
- MIT
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Build neural networks from the perceptron up through backpropagation and regularization techniques
- ✓Model sequences with recurrent networks, attention mechanisms and the transformer architecture
- ✓Implement convolutional networks for computer vision and a working facial detection system
- ✓Train generative models covering autoencoders, VAEs, GANs and diffusion-based approaches
- ✓Apply deep reinforcement learning to control problems and game-playing environments
- ✓Fine-tune a large language model end to end in Lab 3 using Colab GPUs
- ✓Generate music from scratch in Lab 1 by applying sequence models to raw data
- ✓Reason about scaling a training run across many accelerators for massively parallel training
Highlights
- •Rebuilt annually rather than frozen — the 2026 edition adds Secrets to Massively Parallel Training, AI for Science, and The Three Laws of AI
- •Lab 3 fine-tunes an actual LLM, so the practical track ends on current work rather than a decade-old MNIST exercise
- •Every lab runs in Google Colab against a mitdeeplearning pip package: no local CUDA setup, just a Google account and a GPU runtime
- •Lab repository is open at 8.7k stars, 4.6k forks and 736 commits under an MIT license, covering both TensorFlow and PyTorch
- •All slides and lecture recordings are published free on the course site and YouTube while the course is still running
Who It’s For
Best For
- ✓Software engineers moving into ML who want one coherent foundation rather than scattered tutorials
- ✓AI engineers who use LLM APIs daily but never learned the training mechanics underneath them
- ✓Students and self-learners who want a current, credible intro course with runnable labs
- ✓Anyone who needs deep learning breadth — vision, sequences, generative, RL — before specializing
Prerequisites
- •Elementary linear algebra: multiplying matrices and reasoning about vector shapes
- •Elementary calculus: taking derivatives and applying the chain rule
- •Python familiarity is helpful but explicitly not required; a Google account is needed for the Colab labs
FAQ
What is MIT 6.S191: Introduction to Deep Learning (2026)?
MIT's Introduction to Deep Learning is the bootcamp the department rebuilds from scratch every year, and the 2026 edition runs March 30 to May 25 under Alexander and Ava Amini. It takes an engineer who knows linear algebra and some Python from perceptrons all the way to fine-tuning a language model, across nine lectures and three Google Colab labs, entirely free with slides and recordings published as the course runs.
Is MIT 6.S191: Introduction to Deep Learning (2026) free?
MIT 6.S191: Introduction to Deep Learning (2026) is free to access.
What level is MIT 6.S191: Introduction to Deep Learning (2026) for?
MIT 6.S191: Introduction to Deep Learning (2026) is aimed at a beginner audience. Recommended background: Elementary linear algebra: multiplying matrices and reasoning about vector shapes, Elementary calculus: taking derivatives and applying the chain rule, Python familiarity is helpful but explicitly not required; a Google account is needed for the Colab labs.
How long does MIT 6.S191: Introduction to Deep Learning (2026) take?
Expect roughly 9 lectures (~1 hour each) plus 3 Colab labs; ~8 weeks at 3-5 h/week, or self-paced. Most learners work through it at their own pace.
What will I learn from MIT 6.S191: Introduction to Deep Learning (2026)?
You'll learn: Build neural networks from the perceptron up through backpropagation and regularization techniques; Model sequences with recurrent networks, attention mechanisms and the transformer architecture; Implement convolutional networks for computer vision and a working facial detection system; Train generative models covering autoencoders, VAEs, GANs and diffusion-based approaches; Apply deep reinforcement learning to control problems and game-playing environments; Fine-tune a large language model end to end in Lab 3 using Colab GPUs; Generate music from scratch in Lab 1 by applying sequence models to raw data; Reason about scaling a training run across many accelerators for massively parallel training.
Topics
Sources
This page was written from 2 sources, 1 on domains other than introtodeeplearning.com.