Deep Learning Book List

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125 students enrolled

This course consists of:

20 Tutorials

This list is a way for you to track your progress of reading the book (and take back control of want you want to spend your time on).

Subscribe to get started. Choose which days you would like to get reminder emails (if any). The reminder moves on to the next concept only when you mark current one as completed (self-paced).

Syllabus

Chapter 1: Introduction
3 mins
Chapter 2: Linear Algebra
3 mins
Chapter 3: Probability and Information Theory
3 mins
Chapter 4: Numerical Computation
3 mins
Chapter 5: Machine Learning Basics
2 mins
Chapter 6: Deep Feedforward Networks
3 mins
Chapter 7: Regularization for Deep Learning
2 mins
Chapter 8: Optimization for Training Deep Models
2 mins
Chapter 9: Convolutional Networks
2 mins
Chapter 10: Sequence Modeling - Recurrent and Recursive Nets
3 mins
Chapter 11: Practical Methodology
2 mins
Chapter 12: Applications
2 mins
Chapter 13: Linear Factor Models
2 mins
Chapter 14: Autoencoders
2 mins
Chapter 15: Representation Learning
2 mins
Chapter 16: Structured Probabilistic Models for Deep Learning
2 mins
Chapter 17: Monte Carlo Methods
2 mins
Chapter 18: Confronting the Partition Function
2 mins
Chapter 19: Approximate Inference
2 mins
Chapter 20: Deep Generative Models
2 mins

Start this course

Chapter 1: Introduction

Category: Deep Learning Book

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