
Videos Lectures

Lecture 1  First Order Differential Equation

Lecture 2  Gaussian Noise & Brownian Motion

Lecture 3  Stochastic Differential Equation Part 1

Lecture 3  Stochastic Differential Equation Part 2

Lecture 4  Probability, Conditional Probability and Random Variable.

Lecture 5  Kalman Filter Theory and Application

Lecture 51  Kalman Filter  part 1

Lecture 52  Review Stochastic Model and Kalman Filter  part 2

Lecture 53  Kalman Filter  part 3

Lecture 54  Kalman Filter  part 4

Lecture 6  Optimization 1 (unconstrained)

Lecture 7  Linear Algebra 1 (least square)

Lecture 8  Optimization 2 (constrained) part 1

Lecture 8  Optimization 2 (constrained) part 2

Lecture 90  Introduction to Machine Learning

Lecture 91  Classical Machine Learning  part 1

Lecture 92  Classical Machine Learning part 2

Lecture 94  Introduction to Machine Learning  Practice

Lecture 10  Neural Network

Lecture 11  Convolutional Neural Network

Lecture 12  Introduction to Deep Learning

Lecture 121  Recurrent Neural Networks and Transformers

Lecture 122  Convolutional Neural Networks

Lecture 123  Deep Generative Modeling

Lecture 124  Reinforcement Learning

Lecture 125  Deep Learning New Frontiers

Lecture 126  LiDAR for Autonomous Driving

Lecture 127  Automatic Speech Recognition

Lecture 128  AI for Science


Lectures Slide

Lecture 1  First Order Differential Equation

Lecture 2  Gaussian Noise & Brownian Motion

Lecture 3  Stochastic Differential Equation Part 2

Lecture 4  Probability, Conditional Probability and Random Variable.

Lecture 5  Kalman Filter

Lecture 6  Optimization 1 (unconstrained)

Lecture 7  Linear Algebra 1 (least square)

Lecture 8  Optimization 2 (constrained) part 1

Lecture 8  Optimization 2 (constrained) part 2

Lecture 10  Neural Network

Introduction to Machine Learning

Lecture 12  Introduction to Deep Learning
MIT Introduction to Deep Learning 6.S191: Lecture 1
*New 2022 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini
For all lectures, slides, and lab materials: http://introtodeeplearning.com/
Lecture Outline
0:00  Introduction
6:35  Course information
9:51  Why deep learning?
12:30  The perceptron
14:31  Activation functions
17:03  Perceptron example
20:25  From perceptrons to neural networks
26:37  Applying neural networks
29:18  Loss functions
31:19  Training and gradient descent
35:46  Backpropagation
38:55  Setting the learning rate
41:37  Batched gradient descent
43:45  Regularization: dropout and early stopping
47:58  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 fullyconnected!!
*New 2022 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini
For all lectures, slides, and lab materials: http://introtodeeplearning.com/
Lecture Outline
0:00  Introduction
6:35  Course information
9:51  Why deep learning?
12:30  The perceptron
14:31  Activation functions
17:03  Perceptron example
20:25  From perceptrons to neural networks
26:37  Applying neural networks
29:18  Loss functions
31:19  Training and gradient descent
35:46  Backpropagation
38:55  Setting the learning rate
41:37  Batched gradient descent
43:45  Regularization: dropout and early stopping
47:58  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 fullyconnected!!
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