Dive into Deep Learning

---

---

---

[googleapps domain=”drive” dir=”file/d/1BNP4BSodVEcc0ptSNo7cMOYFC4TtbJ7m/preview” query=”” width=”95%” height=”480″ /]

Summary

An interactive deep learning book with code, math, and discussions. Provides both NumPy/MXNet and PyTorch implementations. “We set out to create a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large; and (v) be complemented by a forum for interactive discussion of technical details and to answer questions”.

---

Contents

Preface
Installation
Notation

1. Introduction
2. Preliminaries
   keyboard_arrow_down
3. Linear Neural Networks
   keyboard_arrow_down
4. Multilayer Perceptrons
   keyboard_arrow_down
5. Deep Learning Computation
   keyboard_arrow_down
6. Convolutional Neural Networks
   keyboard_arrow_down
7. Modern Convolutional Neural Networks
   keyboard_arrow_down
8. Recurrent Neural Networks
   keyboard_arrow_down
9. Modern Recurrent Neural Networks
   keyboard_arrow_down
10. Attention Mechanisms
    keyboard_arrow_down
11. Optimization Algorithms
    keyboard_arrow_down
12. Computational Performance
    keyboard_arrow_down
13. Computer Vision
    keyboard_arrow_down
14. Natural Language Processing: Pretraining
    keyboard_arrow_down
15. Natural Language Processing: Applications
    keyboard_arrow_down
16. Recommender Systems
    keyboard_arrow_down
17. Generative Adversarial Networks
    keyboard_arrow_down
18. Appendix: Mathematics for Deep Learning
    keyboard_arrow_down
19. Appendix: Tools for Deep Learning

---

Authors

Chapter Authors

#R0identifier=”74aa611d2dc64c81a4216b65509d6664”

Liked this post? Follow this blog to get more.