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The Deep Learning Architect's Handbook

You're reading from  The Deep Learning Architect's Handbook

Product type Book
Published in Dec 2023
Publisher Packt
ISBN-13 9781803243795
Pages 516 pages
Edition 1st Edition
Languages
Author (1):
Ee Kin Chin Ee Kin Chin
Profile icon Ee Kin Chin

Table of Contents (25) Chapters

Preface 1. Part 1 – Foundational Methods
2. Chapter 1: Deep Learning Life Cycle 3. Chapter 2: Designing Deep Learning Architectures 4. Chapter 3: Understanding Convolutional Neural Networks 5. Chapter 4: Understanding Recurrent Neural Networks 6. Chapter 5: Understanding Autoencoders 7. Chapter 6: Understanding Neural Network Transformers 8. Chapter 7: Deep Neural Architecture Search 9. Chapter 8: Exploring Supervised Deep Learning 10. Chapter 9: Exploring Unsupervised Deep Learning 11. Part 2 – Multimodal Model Insights
12. Chapter 10: Exploring Model Evaluation Methods 13. Chapter 11: Explaining Neural Network Predictions 14. Chapter 12: Interpreting Neural Networks 15. Chapter 13: Exploring Bias and Fairness 16. Chapter 14: Analyzing Adversarial Performance 17. Part 3 – DLOps
18. Chapter 15: Deploying Deep Learning Models to Production 19. Chapter 16: Governing Deep Learning Models 20. Chapter 17: Managing Drift Effectively in a Dynamic Environment 21. Chapter 18: Exploring the DataRobot AI Platform 22. Chapter 19: Architecting LLM Solutions 23. Index 24. Other Books You May Enjoy

Understanding non-RL-based NAS

The core of NAS is about intelligently searching through different child architecture configurations by making decisions based on prior search experience to find the best child architecture in a non-random and non-brute-force way. The core of RL, on the other hand, involves utilizing a controller-based system to achieve that intelligence. Intelligent NAS can be achieved without using RL, and in this section, we will go through a simplified version of the progressive growing-from-scratch style of NAS without a controller and another competitive version of elimination from a complex fully defined NN macroarchitecture and microarchitecture.

Understanding path elimination-based NAS

First and foremost, differentiable architecture search (DARTS) is a method that extends the DAG search space defined in ENAS by removing the RL controller component. Instead of choosing previous nodes to connect to and choosing which operation to use for a node, all operations...

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