MIT Introduction to Deep Learning | 6.S191

MIT Introduction to Deep Learning 6.S191: Lecture 1
*New 2024 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini

Lecture Outline
0:00​ - Introduction
7:25​ - Course information
13:37​ - Why deep learning?
17:20​ - The perceptron
24:30​ - Perceptron example
31;16​ - From perceptrons to neural networks
37:51​ - Applying neural networks
41:12​ - Loss functions
44:22​ - Training and gradient descent
49:52​ - Backpropagation
54:57​ - Setting the learning rate
58:54​ - Batched gradient descent
1:02:28​ - Regularization: dropout and early stopping
1:08:47 - Summary

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[keynadaby]

It's wonderful to see universities of the calliber of MIT making education accessible to everyone for free. Thanks MIT!!

[elaina]

I am a high school student and I am currently self-studying deep learning and I find it very helpful.
I hope one day I can attend your lectures in person.
Thank you very much.

[sftmain]

After being in college for 4 years and dealing with loads of professors, I can hands down say this guy is the best lecturer I've ever seen! Explains tough concepts so well.

[issamsum]

I usually find neural networks challenging to grasp until I watched this lecture. I truly appreciate how you simplified the concept for me.

[nomthandazombatha]

I want to take this moment to thank YouTube, MIT and Alexander Amini for suppling this content 4 a person like me who is studding deep learning but was not fortunate enough to study in MIT🙏🙏

[prasmitdevkota]

What a privilege and great time we live in that most precious courses like these from MIT are accessible for freee.

[jazonsamillano]

I've been following these MIT Deep Learning lectures since 2019. I've learned so much. Thank you, Alexander and Ava.

[genkideska]

This is not for beginners. Having 3+ years of experience in deep learning i found it interesting on how much information is shoved into 1 single video . Note that each concept is very vast if we dig deeper

[ReflectionOcean]

By "YouSum Live"

00:00:10 Introduction to MIT course on deep learning
00:00:41 Evolution of AI and deep learning
00:02:56 Realism and virality of AI-generated content
00:04:15 Accessibility and cost-effectiveness of deep learning
00:04:19 Advancements in deep learning applications
00:05:11 Empowering deep learning models to create software
00:06:02 Teaching foundations of deep learning
00:07:30 Importance of understanding intelligence and AI
00:14:00 Transition from hand-engineered features to deep learning
00:16:00 Significance of data, compute power, and software in deep learning
00:17:24 Fundamentals of a neural network: the perceptron
00:19:59 Mathematical representation of a perceptron
00:20:51 Activation function and its role in neural networks
00:20:57 Importance of activation functions in neural networks
00:21:11 Sigmoid function: squashes inputs into probabilities
00:23:01 Need for nonlinearity in neural networks
00:23:32 Linear functions insufficient for handling nonlinear data
00:24:22 Nonlinearities enhance neural network expressiveness
00:26:01 Visualizing neural network's decision-making process
00:27:33 Sigmoid function divides space for classification
00:28:16 Understanding feature space in neural networks
00:29:21 Building neural networks step by step
00:31:41 Perceptron's fundamental equation: dot product, bias, nonlinearity
00:32:49 Defining layers and passing information in neural networks
00:37:19 Cascading layers to create deep neural networks
00:38:18 Applying neural networks to real-world problems
00:40:38 Neural network training process explained
00:40:50 Neural networks learn like babies, need data
00:41:12 Teaching neural network to make correct decisions
00:41:32 Importance of minimizing loss for accurate models
00:41:55 Training neural network with data from multiple students
00:42:21 Finding network that minimizes empirical loss
00:42:40 Using softmax function for binary classification
00:43:27 Loss function for real-valued outputs
00:47:56 Gradient descent for optimizing neural network weights
00:59:51 Introduction to gradient descent algorithms
01:00:11 Stochastic gradient descent (SGD) explained
01:00:45 Importance of mini-batch gradient descent
01:01:37 Faster convergence with mini-batches
01:02:03 Parallelization benefits of mini-batches
01:02:30 Understanding overfitting in machine learning
01:04:41 Regularization techniques: Dropout and early stopping
01:06:56 Monitoring training and testing accuracy
01:08:48 Summary of key points in neural network fundamentals

By "YouSum Live"

[andreluizleitejunior]

Thank you, Alexander and MIT for make this information available for everyone.

[MohanadMala]

The clarity you are providing for such a complix scientific subject is remarkable 👏

[lelsewherelelsewhere]

Both theory and actual implementation in industry code! Perfect! Also, great pacing and depth!
After 5 minutes in one episode, and i can already tell this is the best beginner ai lecture series I have seen!

[c-spacetime]

Yesterday we started system identification using neural network, I watched your lecture and now I feel quite comfortable using the concept of deep learning. Thank you Sir and love from Pakistan....

[dantedt]

This is prolly the best Deep Learning lesson out there. With some maths or stats background, it's easy to follow. This is gold!

[paultvshow]

Hands down, this is the best low level explanation of deep neural networks I have seen so far.

[PureClarityAbsolute]

Attended Deep Learning lectures at a topmost college of a country, here he clearly explained all that in a single lecture for which the former took 10s of lectures to explain.

[fayezfamfa]

Really thank you Dr.Alex for making this material accessible to everyone

[premprakash]

Thankyou Alex, this was really a great foundational course on Neural Networks. Will continue with other uploads in this series.

[DennisZIyanChen]

I look at these videos every year after the new annual release and it just never gets old. Too bad in my work, I don't get a chance to apply this knowledge. It is still super fun to watch, like a fun show to me

[saffanahmedkhan]

How fascinating is it i wanted to learn about neural networks and just searched neural networks mit and found a course thankyou so much youtube and MIT.