Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

MIT Introduction to Deep Learning

Alexander Amini and Massachusetts Institute of Technology via YouTube

Overview

Dive into the foundations of deep learning with this comprehensive lecture from MIT's Introduction to Deep Learning course. Explore key concepts including perceptrons, neural networks, activation functions, loss functions, gradient descent, backpropagation, and regularization techniques. Learn why deep learning is revolutionizing artificial intelligence and gain practical insights into applying neural networks. Follow along as the lecturer guides you through examples, explains training processes, and discusses important considerations like learning rates and batched gradient descent. By the end of this 49-minute session, acquire a solid understanding of the fundamental principles underlying modern deep learning approaches.

Syllabus

​ - Introduction
​ - Course information
​ - Why deep learning?
​ - The perceptron
​ - Activation functions
​ - Perceptron example
​ - From perceptrons to neural networks
​ - Applying neural networks
​ - Loss functions
​ - Training and gradient descent
​ - Backpropagation
​ - Setting the learning rate
​ - Batched gradient descent
​ - Regularization: dropout and early stopping
​ - Summary

Taught by

https://www.youtube.com/@AAmini/videos

Reviews

Start your review of MIT Introduction to Deep Learning

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.