MathematicalEngineeringDeepLearning

Material for The Mathematical Engineering of Deep Learning. See the actual book content on deeplearningmath.org or (when it is out) purchase the book from CRC press.

This repository contains general supporting material for the book.

Below is a detailed list of the source code used for creating figures and tables in the book. We use Julia, Python, or R and the code is sometimes in stand alone files, sometimes in Jupyter notebooks, sometimes as R Markdown, and sometimes in Google Colab. Many of our static illustrations were created using TikZ by Ajay Hemanth and Vishnu Prasath with the source of their illustrations also available so you can adapt it for purposes.

Chapter 1

Figure	Topic	Source Code
1.1	Fast.ai example	Python Google Colab
1.3	Architectures	TikZ(a), TikZ(b), TikZ(c), TikZ(d), TikZ(e), TikZ(f)
1.4	Neurons	TikZ(b), TikZ(d)
1.5	Data on earth	Julia

Chapter 2

Figure	Topic	Source Code
2.1	Supervised Learning	TikZ
2.2	Unsupervised Learning	TikZ
2.3	Simple regression	R
2.4	Breast Cancer ROC curves	R
2.5	Least Squares	TikZ
2.6	Loss functions	Julia
Table 2.1	Linear MNIST classification	Julia
2.7	Gradient Descent Learning Rate	Python
2.8	Loss Landscape	R
2.9	Generalization and Training	TikZ or Julia
2.10	Polynomial fit	R
2.11	K-fold cross validation	TikZ
2.12	K-means clustering	R
2.13	K-means image segmentation	R
2.14	Breast Cancer PCA	R
2.15	SVD Compression	Julia

Chapter 3

Figure	Topic	Source Code
3.1	TBD

Chapter 4

Figure	Topic	Source Code
4.1	TBD

Chapter 5

Figure	Topic	Source Code
5.1	TBD

Chapter 6

Figure	Topic	Source Code
6.1	TBD

Chapter 7

Figure	Topic	Source Code
7.1	TBD

Chapter 8

Figure	Topic	Source Code
8.1	TBD

Chapter 9

Figure	Topic	Source Code
9.1	TBD

Chapter 10

Figure	Topic	Source Code
10.1	TBD