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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

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781803243795
Length 516 pages
Edition 1st Edition
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Author (1):
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Ee Kin Chin Ee Kin Chin
Author Profile Icon Ee Kin Chin
Ee Kin Chin
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Table of Contents (25) Chapters Close

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 24. Other Books You May Enjoy

Exploring LLM solution use cases

In this section, we will explore some fascinating real-world applications where LLM solutions can truly shine. This will give you a sense of how revolutionary LLM solutions are. The use cases are as follows:

  • Travel itinerary planner: LLMs can be employed to develop advanced travel itinerary planners that generate personalized trip plans based on user preferences and constraints. By integrating LLMs with travel APIs, such as flight, hotel, and attraction databases, as well as real-time data sources such as weather and traffic information, these planners can provide context-aware recommendations tailored to individual traveler needs. Notably, companies such as Booking.com and Expedia integrated this into their products, and Agoda announced that they will be working on it.
  • Intelligent tutoring systems: LLMs can be used to develop intelligent tutoring systems that offer personalized learning experiences for students. By integrating LLMs with...
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