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Video status: Videos included. Source details
Lecture 1 of MIT 6.S191 2026 is The Minimal Structure of Deep Learning. It builds the vocabulary shared by the rest of the course: perceptrons, forward propagation, loss, and gradient descent. This note uses only the official 2026 slides and video; it does not mix in similarly named material from 2025.
What to take away
- Write one neuron as a weighted sum, bias, and nonlinear activation
- Track tensor shapes through layers instead of memorizing architecture names
- Explain what a loss measures and how gradient descent changes parameters
These goals have one thing in common: recognizing terminology is insufficient. You should be able to identify inputs, outputs, the learning signal, and the main constraint before moving on.
A complete chain is: inputs pass through weighted sums and activations to form a prediction; a loss turns prediction-versus-target into a scalar; backpropagation uses the chain rule to assign responsibility to each parameter. The limit is that a falling loss only improves the chosen objective—it does not guarantee out-of-distribution or real-world performance.
How to watch
First scan the sections and diagrams in the official slides, then watch the official video. On a second pass, pause at equations and architecture diagrams and redraw them in your own notation. Afterward, close the material and write three central ideas plus one unresolved question.
An exercise for tonight
After watching, draw a two-layer network and label every tensor shape, then use Lab 1 Part 1 to verify the matrix multiplications.
“Finished” means leaving a checkable diagram, calculation, program output, or short note—not merely reaching the end of the video. You should also be able to explain one failure mode to someone else.
Scope and limits
6.S191 is a high-intensity introduction, and this article is only a lecture guide. It does not replace the full recording, rigorous derivations, or instructor feedback. Use a semester course or primary papers when a topic needs theoretical depth.
Course video sources
Rechecked against the live official course page on 2026-10-10: the lecture numbers and recording links match and the videos are public and embeddable.
Original videos: MIT Introduction to Deep Learning | 6.S191
Course and recording entries:
Checked: 2026-10-10.
Content check: verified against the video transcript (2026-10-10): the video is Alexander Amini's opening lecture; the transcript covers the perceptron (single neuron, activation), loss, gradient descent, backpropagation (chain rule) and overfitting, matching the concept chain in this note, and introduces the labs.
Update Log
- 2026-10-10: Added explicit video status and checked recording sources and access notes.
- 2026-10-10: Rechecked video status. Live-checked the official course page: lecture numbers and recording links match and the videos are public, so the status is now “Videos included.”
- 2026-10-10: Checked the video content against its transcript. The topic and speaker match; nothing needed correcting.
References
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