Helpful Resources for Reading the Book 'Deep Learning'
This article is a translated version of my original post on Qiita. Original (Japanese): https://qiita.com/segur/items/10b7b41f63bf53e79206
Introduction
As I read through the book "Deep Learning," I relied heavily on a variety of online resources. I am summarizing them below as a reference. I am deeply grateful to the authors of these resources for providing such clear and insightful information.
Amazon / Deep Learning (Machine Learning Professional Series)
Chapter 1: Introduction
Chapter 2: Feedforward Networks
- https://youtu.be/L_6idb3ZXB0
- https://youtu.be/w9OFiOaTFs8
- Chapter 02 #ml-professional
- Deep Learning chap.1 and 2
- Binary and Multi-Class Classification
Chapter 3: Stochastic Gradient Descent
- https://youtu.be/5g0TPrxKK6o
- What is Stochastic Gradient Descent? Explained with Python
- Deep Learning chap.3_1
- Deep Learning chap.3_2
- Optimization Algorithms for Gradient Descent
Chapter 4: Backpropagation
Chapter 5: Autoencoders
- Deep Learning chap.5_1
- Deep Learning chap.5_2
- Reading Club for Machine Learning Professional Series #2 Chapter 5 "Autoencoders" Material
Chapter 6: Convolutional Neural Networks
- How Do Convolutional Neural Networks Work?
- Understanding Convolutional Neural Networks (Part 1)
- Understanding Convolutional Neural Networks from Scratch
- Stanford Wiki / Convolution Schematic
- Deep Learning Chapter 6: Convolutional Neural Networks
Chapter 7: Recurrent Neural Networks
Chapter 8: Boltzmann Machines
- Restricted Boltzmann Machines - Ep. 6 (Deep Learning SIMPLIFIED)
- Beginner's Guide to Restricted Boltzmann Machines
- Chapter 8 Boltzmann Machines - Deep Learning Book Reading Group
Conclusion
I managed to get through the book successfully. I feel that my mathematical skills have slightly improved.