Deep Learning

이 책은 딥 러닝의 기초부터 비교적 최근 모델들까지 전반적인 딥러닝에 대한 내용을 다루고 있으며 목차는 다음과 같습니다. 본 포스트 시리즈에서는 Deep Learning을 공부한 후, 다음 목차별로 정리할 계획입니다.

1. Introduction

   Part I: Applied Math and Machine Learning Basics

2. Linear Algebra
3. Probability and Information Theory
4. Numerical Computation
5. Machine Learning Basics

   Part II: Modern Practical Deep Networks

6. Deep Feedforward Networks
7. Regularization for Deep Learning
8. Optimization for Training Deep Models
9. Convolutional Networks
10. Sequence Modeling: Recurrent and Recursive Nets
11. Practical Methodology
12. Applications

    Part III: Deep Learning Research

13. Linear Factor Models
14. Autoencoders
15. Representation Learning
16. Structured Probabilistic Models for Deep Learning
17. Monte Carlo Methods
18. Confronting the Partition Function
19. Approximate Inference
20. Deep Generative Models

References

[1] Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, 2016 [site]