MIT Introduction to Deep Learning | 6.S191
MIT Introduction to Deep Learning 6.S191: Lecture 1
*New 2023 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini
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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Видео MIT Introduction to Deep Learning | 6.S191 канала Alexander Amini
*New 2023 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini
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
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us on @MITDeepLearning on Twitter and Instagram to stay fully-connected!!
Видео MIT Introduction to Deep Learning | 6.S191 канала Alexander Amini
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