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
Lesson 2 – Linear Algebra
Lesson 3 – Probability and Information Theory
Lesson 4 – Numerical Computation
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!
Lesson 6 – Deep Feedforward Networks
Lesson 7 – Regularization for Deep Learning
Lesson 8 – Optimization for Training Deep Models
Lesson 9 – Convolutional Networks
Lesson 10 – Sequence Modeling: Recurrent and Recursive Networks
Lesson 11 – Practical Methodology
Lesson 12 – Applications
Lesson 13 – Linear Factors
Lesson 14 – Autoencoders
Lesson 15 – Representation Learning
Lesson 16 – Structured Probabilistic Models for Deep Learning
Lesson 17 – Monte Carlo Methods
Lesson 18 – Confronting the Partition Function
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!
🧑💻 Recommended: Gradient Descent in Neural Nets – A Simple Guide to ANN Learning
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