Massachusetts Institute of Technology (introtodeeplearning.com)
MIT 6.S191 Introduction to Deep Learning Review — Honest Analysis
MIT 6.S191 is the strongest free short-format introduction to modern deep learning in 2026 — eight intensive lectures and three Colab labs taught by Alexander Amini and Ava Amini, refreshed every January with the year's actual frontier (LLMs, diffusion, AI for science). It is not a comprehensive multi-month curriculum. Take it as a survey of where the field is now, not as a substitute for Andrew Ng's specialization or Fast.ai.
Final score
from 33 analysed opinions
Published AI-researched, editor-audited
Distribution of opinions
Per-criterion scores
Reviewers consistently praise that the curriculum is refreshed annually and reaches modern topics — Transformers, generative modeling, LLMs, AI for science — that older courses do not cover. The honest catch is that depth is sacrificed for breadth in eight lectures.
Alexander Amini is described as clear, energetic and good at building intuition from first principles. The recurring caveat is the rotating-lecturer format — multiple reviewers wish Amini taught every lecture rather than alternating with guests and co-instructors.
Completely free — lectures on YouTube, slides on introtodeeplearning.com, labs on GitHub, runnable in free Google Colab. No paywall on any core material. The optional MIT Professional Certificate is not the path most reviewers take.
There is no official forum for online learners. Reviewers credit the GitHub issue tracker as the de facto Q&A channel, but multiple 2024-2025 issues report unresolved bugs in the PyTorch Sequential labs and outdated Colab dependencies.
Three Colab labs (music generation, vision, LLMs) are short but hands-on in both TensorFlow and PyTorch. Reviewers note this is a foundation, not a job-ready portfolio — you finish with intuition and small projects, not a deployed model.
What learners said
What people loved
6- Refreshed every year with the actual frontier — recent editions cover Transformers, LLMs, diffusion models and AI for science before most other MOOCs catch up×14
- Completely free on YouTube and introtodeeplearning.com — slides, lecture videos and Colab labs with no paywall×12
- Short and intensive — eight lectures total, finishable in one or two weeks, which makes it realistic to actually complete×9
- Labs support both TensorFlow and PyTorch and run in free Google Colab with no setup×6
- Alexander Amini's lecture style is clear, well-paced and good at building intuition from first principles×8
- MIT brand and production quality — captioned, well-edited videos with consistent slide design across the series×5
What frustrated learners
5- Short format means depth is shallow — eight one-hour lectures cannot replace a full multi-month deep learning curriculum×10
- Real prerequisites (linear algebra, calculus, comfortable Python) despite the "no prior experience" framing — beginners report bouncing off×7
- Rotating co-lecturers and guest speakers create uneven pacing — several reviewers explicitly wish Amini taught the full course×5
- PyTorch lab bugs in the Sequential examples reported repeatedly in the GitHub issue tracker without resolution×4
- No official forum or Q&A channel for online learners — GitHub issues are the de facto support and response is slow×3
Real quotes from real users
“This hour-long MIT lecture is very good, it builds from the ground up until transformers. MIT 6.S191 Recurrent Neural Networks, Transformers, and Attention.”
“Discovering this kind of ressources is a big reason I come on HN. Another recent and good one on the same topic is MIT 6.S191.”
“I've been finding 6.S191 a pretty good introduction to the landscape. I wish Alexander taught the entire course instead of switching off each lecture, but overall I'm enjoying it.”
“Looks neat. Personally bummed that it goes with Tensorflow, though I guess that may be related to the course being sponsored in part by Google. Pretty much all the latest research is being published in Pytorch and even OpenAI switched to Pytorch recently.”
“I love how much effort MIT is putting in making these courses widely available (i.e. releasing slides, video and exercises online).”
Frequently asked questions
How we evaluated this
This review synthesizes 33 opinions collected across the public web. Final score = Bayesian average penalising small samples, then weighted by the positivity ratio. No paid placements, no hidden agenda.
- 22 from Hacker News
- 7 from Blogs
- 4 from Forums