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MIT Introduction to Deep Learning (2023) | 6.S191

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MIT Introduction to Deep Learning 6.S191: Lecture 1
Foundations of Deep Learning
Lecturer: Alexander Amini
2023 Edition

For all lectures, slides, and lab materials: http://introtodeeplearning.com/

Lecture Outline
0:00​ - Introduction
8:14 ​ - Course information
11:33​ - Why deep learning?
14:48​ - The perceptron
20:06​ - Perceptron example
23:14​ - From perceptrons to neural networks
29:34​ - Applying neural networks
32:29​ - Loss functions
35:12​ - Training and gradient descent
40:25​ - Backpropagation
44:05​ - Setting the learning rate
48:09​ - Batched gradient descent
51:25​ - Regularization: dropout and early stopping
57:16​ - Summary

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