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The Deep Learning Architect's Handbook

You're reading from The Deep Learning Architect's Handbook Build and deploy production-ready DL solutions leveraging the latest Python techniques

Product type Paperback

Published in Dec 2023

Publisher Packt

ISBN-13 9781803243795

Length 516 pages

Edition 1st Edition

Languages

Python

Tools

BERT

Concepts

Deep Learning

Author (1):

Ee Kin Chin

View More author details

Table of Contents (25) Chapters

Preface

1. Part 1 – Foundational Methods

2. Chapter 1: Deep Learning Life Cycle FREE CHAPTER

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

Why subscribe?

24. Other Books You May Enjoy

Technical requirements

This chapter will cover a practical example to test out data drift techniques. We will be using Python 3.10 and, additionally, we will require the following Python libraries to be installed:

evidently
numpy
transformers==4.21.3
torch==1.12.0
syllables
audiomentations
datasets

The code files are available on GitHub: https://github.com/PacktPublishing/The-Deep-Learning-Architect-Handbook/tree/main/CHAPTER_17.

The rest of the chapter is locked

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Authors (1)

Ee Kin Chin

Ee Kin Chin

Ee Kin Chin is a Senior Deep Learning Engineer at DataRobot. He holds a Bachelor of Engineering (Honours) in Electronics with a major in Telecommunications. Ee Kin is an expert in the field of Deep Learning, Data Science, Machine Learning, Artificial Intelligence, Supervised Learning, Unsupervised Learning, Python, Keras, Pytorch, and related technologies. He has a proven track record of delivering successful projects in these areas and is dedicated to staying up to date with the latest advancements in the field.

See other products by Ee Kin Chin

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