The Deep Learning Architect's Handbook

Table of Contents

1. Deep Learning Life Cycle
2. Designing Deep Learning Architectures
3. Understanding Convolutional Neural Networks
4. Understanding Recurrent Neural Networks
5. Understanding Autoencoders
6. Understanding Neural Network Transformers
7. Deep Neural Architecture Search
8. Exploring Supervised Deep Learning
9. Exploring Unsupervised Deep Learning
10. Exploring Model Evaluation Methods
11. Explaining Neural Network Predictions
12. Interpreting Neural Network
13. Exploring Bias and Fairness
14. Analyzing Adversarial Performance
15. Deploying Deep Learning Models in Production
16. Governing Deep Learning Models
17. Managing Drift Effectively in a Dynamic Environment
18. Exploring the DataRobot AI Platform
19. Architecting LLM Solutions