This is the first lecture of MIT 6.S191, the institute’s introductory deep learning course, in its 2024 edition delivered by lead instructor Alexander Amini. The course is known for compressing a full introduction to deep learning into a fast-paced series of lectures, and this opening session sets the foundation.
The lecture builds up from the perceptron, the simplest model of a neuron, to multi-layer networks, explaining how they are trained with gradient descent and backpropagation. It covers the practical concerns that come up immediately in real training, such as overfitting and how to mitigate it, and it previews the sequence models and architectures that later lectures explore in depth.
As a current, well-produced university treatment, this lecture is a strong entry point for someone who wants a structured course rather than a single explainer. It carries the credibility of an MIT course while remaining accessible to a motivated beginner, and the full series is freely available for those who want to continue.