Dive Into Deep Learning

This course is for students (undergraduate or graduate), engineers, and researchers, who seek a solid grasp of the practical techniques of deep learning. Because we explain every concept from scratch, no previous background in deep learning or machine learning is required. Fully explain- ing the methods of deep learning requires some mathematics and programming, but we will only assume that you come in with some basics, including modest amounts of linear algebra, calculus, probability, and Python programming. Just in case you forget the basics, the Appendix provides a refresher on most of the mathematics you will find in this book. Most of the time, we will pri- oritize intuition and ideas over mathematical rigor. If you would like to extend these foundations beyond the prerequisites to understand our book, we happily recommend some other terrific re- sources: Linear Analysis by Bela Bollobas (Bollobas, 1999) covers linear algebra and functional analysis in great depth. All of Statistics (Wasserman, 2013) provides a marvelous introduction to statistics. Joe Blitzsteinʼs books3 and courses4 on probability and inference are pedagogical gems. And if you have not used Python before, you may want to peruse this Python tutorial5.