Diving Deep into ‘Deep Learning’ – An 18-Video Guide by Ian Goodfellow and Experts

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Welcome to the ultimate video guide on the groundbreaking book “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville!

Why “Deep Learning” the book? It’s the definitive textbook in the field, “Deep Learning” covers a comprehensive range of topics, from the foundational concepts to the advanced techniques driving the latest innovations in artificial intelligence.

Why watching videos instead of reading the book? I found the world of deep learning to be a complex maze of mathematical equations and abstract concepts. However, watching Ian Goodfellow and other experts break down each chapter with real-world examples and intuitive explanations transformed my perspective. Watching videos is like having a personal tutor guiding me through the intricacies of neural networks, making the once-daunting subject both accessible and fascinating.

That’s why I have chosen one video for each book chapter, preferably from one of the authors, so you can “read the book” in a more interactive multimodal format.

Lesson 1 – Introduction

Deep Learning Chapter 1 Introduction presented by Ian Goodfellow

πŸ”— Read Chapter

Lesson 2 – Linear Algebra

Deep Learning Chapter 2 Linear Algebra presented by Gavin Crooks

πŸ”— Read Chapter

Lesson 3 – Probability and Information Theory

Deep Learing Chapter 3 Probability presented by Pierre Dueck
Deep Learning Chapter 3 Information Theory presented by Yaroslav Bulatov

πŸ”— Read Chapter

Lesson 4 – Numerical Computation

Ian Goodfellow - Numerical Computation for Deep Learning - AI With The Best Oct 14-15, 2017

πŸ”— Read Chapter

Lesson 5 – Machine Learning Basics

This is a playlist based on Chapter 5 of the Deep Learning book, if you need a quick refresher on ML, feel free to watch the whole playlist!

πŸ”— Read Chapter

Lesson 6 – Deep Feedforward Networks

Deep Learning Book Chapter 6, ""Deep Feedforward Networks" presented by Ian Goodfellow

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Lesson 7 – Regularization for Deep Learning

Lecture 7 - Regularization for deep learning

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Lesson 8 – Optimization for Training Deep Models

Optimization for Deep Learning - Ian Goodfellow GAN inventor

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Lesson 9 – Convolutional Networks

Ch 9: Convolutional Networks

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Lesson 10 – Sequence Modeling: Recurrent and Recursive Networks

Deep Learning Chapter 10 Sequence Modeling: Recurrent and Recursive Nets presented by Ian Goodfellow

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Lesson 11 – Practical Methodology

Ian Goodfellow, Google - Practical Methodology for Deploying Machine Learning #AIWTB Oct 2015

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Lesson 12 – Applications

Ian Goodfellow: Generative Adversarial Networks (GANs) | Lex Fridman Podcast #19

πŸ”— Read Chapter

Lesson 13 – Linear Factors

An Introduction to Linear Factor Models

πŸ”— Read Chapter

Lesson 14 – Autoencoders

What are Autoencoders?

πŸ”— Read Chapter

Lesson 15 – Representation Learning

Live Stream Chapter 15: Representation Learning with Cosmin Negruseri

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Lesson 16 – Structured Probabilistic Models for Deep Learning

Structured Probabilistic Models in Deep Learning

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Lesson 17 – Monte Carlo Methods

Monte Carlo Methods

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Lesson 18 – Confronting the Partition Function

Confronting the Partition Function

πŸ”— Read Chapter


Congratulations, you’ve just reached the top elite coders proficient in theory and practice of the most essential invention in computer science — deep neural networks!

πŸ§‘β€πŸ’» Recommended: Gradient Descent in Neural Nets – A Simple Guide to ANN Learning

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