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MIT Deep Learning Basics: Introduction and Overview

Artis Modus



Lex Fridman

An introductory lecture for MIT course 6.S094 on the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.

INFO:
Website: https://deeplearning.mit.edu
GitHub: https://github.com/lexfridman/mit-deep-learning
Slides: http://bit.ly/deep-learning-basics-slides
Playlist: http://bit.ly/deep-learning-playlist
Blog post: https://link.medium.com/TkE476jw2T

OUTLINE:
0:00 – Introduction
0:53 – Deep learning in one slide
4:55 – History of ideas and tools
9:43 – Simple example in TensorFlow
11:36 – TensorFlow in one slide
13:32 – Deep learning is representation learning
16:02 – Why deep learning (and why not)
22:00 – Challenges for supervised learning
38:27 – Key low-level concepts
46:15 – Higher-level methods
1:06:00 – Toward artificial general intelligence

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36 thoughts on “MIT Deep Learning Basics: Introduction and Overview”

  14. Hello ,I'm a engeenering student that is looking to work in AI later I would be interested in more IA related videos, publications, articles or books ; Thanks to anyone who could provide me some guidance !

  20. Great lecture, Lex! One comment, though: at 38:48, you should mention that the MSE is defined with respect to all samples, whereas the term you give as the definition of cross-entropy only refers to a single sample. Confusion could arise because you use the same index i, once for the samples and once for the classes. PS: I am going to watch all your lecture videos. 🙂

  23. Deep learning models are widely used in different fields due to its capability to handle large and complex datasets and produce the desired results with more accuracy at a greater speed. In Deep learning models, features are selected automatically through the iterative process wherein the model learns the features by going deep into the dataset and selects the features to be modeled. In the traditional models the features of the dataset needs to be specified in advance. The Deep Learning algorithms are derived from Artificial Neural Network concepts and it is a part of broader Machine Learning Models.

    This book intends to provide an overview of Deep Learning models, its application in the areas of image recognition & classification, sentiment analysis, natural language processing, stock market prediction using R statistical software package, an open source software package.

    The book also includes an introduction to python software package which is also open source software for the benefit of the users.

    This books is a second book in series after the author’s first book- Machine Learning: An Overview with the Help of R Software https://www.amazon.com/dp/B07KQSN447

    Editor

    International Journal of Statistics and Medical Informatics

    http://www.ijsmi.com/book.php

    Amazon Link

    https://www.amazon.com/dp/B07NJMM6LR

  32. 37:13 "A neural network with a single hidden layer can approximate any (arbitrary) function"
    Is this true? Can it approximate a function where an input is squared, cubed, etc? Or a sine fn?
    Seems like it would depend on the activation function a lot, it seems like it wouldn't be true with a Relu activation.
    I honestly don't know if this is true, so just asking…

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