Table of Contents

1. Introduction to Deep Learning Programming
2. Coding Deep Learning with Modern C++
3. Testing Deep Learning Code
4. Implementing Convolutions
5. Coding the Fully Connected Layer
6. Learning by Minimizing Cost Functions
7. Defining Activation Functions
8. Using Pooling Layers
9. Coding the Gradient Descent Algorithm
10. Coding the Backpropagation Algorithm
11. Underfitting, Overfitting, and Regularization
12. Implementing Cross-validation, Mini Batching, and Model Performance Metrics
13. Implementing Optimizers
14. Introducing Computer Vision Models
15. Developing an Image Classifier
16. Leveraging Training Performance with Transfer Learning
17. Developing an Object Localization System