Study - Deep Learning

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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


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