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Building AI Applications with ChatGPT APIs

You're reading from   Building AI Applications with ChatGPT APIs Master ChatGPT, Whisper, and DALL-E APIs by building ten innovative AI projects

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Product type Paperback
Published in Sep 2023
Publisher
ISBN-13 9781805127567
Length 258 pages
Edition 1st Edition
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Concepts
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Author (1):
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Martin Yanev Martin Yanev
Author Profile Icon Martin Yanev
Martin Yanev
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Table of Contents (19) Chapters Close

Preface 1. Part 1:Getting Started with OpenAI APIs
2. Chapter 1: Beginning with the ChatGPT API for NLP Tasks FREE CHAPTER 3. Chapter 2: Building a ChatGPT Clone 4. Part 2: Building Web Applications with the ChatGPT API
5. Chapter 3: Creating and Deploying an AI Code Bug Fixing SaaS Application Using Flask 6. Chapter 4: Integrating the Code Bug Fixer Application with a Payment Service 7. Chapter 5: Quiz Generation App with ChatGPT and Django 8. Part 3: The ChatGPT, DALL-E, and Whisper APIs for Desktop Apps Development
9. Chapter 6: Language Translation Desktop App with the ChatGPT API and Microsoft Word 10. Chapter 7: Building an Outlook Email Reply Generator 11. Chapter 8: Essay Generation Tool with PyQt and the ChatGPT API 12. Chapter 9: Integrating ChatGPT and DALL-E API: Build End-to-End PowerPoint Presentation Generator 13. Chapter 10: Speech Recognition and Text-to-Speech with the Whisper API 14. Part 4:Advanced Concepts for Powering ChatGPT Apps
15. Chapter 11: Choosing the Right ChatGPT API Model 16. Chapter 12: Fine-Tuning ChatGPT to Create Unique API Models 17. Index 18. Other Books You May Enjoy

Fine-Tuning ChatGPT

In this section, you will learn about the process of fine-tuning ChatGPT models. We will talk about the ChatGPT models available for fine-tuning and provide information on their training and usage costs. We will also cover the installation of the openai library and set up the API key as an environmental variable in the terminal session. This section will serve as an overview of fine-tuning, its benefits, and the necessary setup to train a fine-tuned model.

Fine-tuning enhances the capabilities of API models in several ways. Firstly, it yields higher-quality outcomes compared to designing prompts alone. By incorporating more training examples than can be accommodated in a prompt, fine-tuning enables models to grasp a wider range of patterns and nuances. Secondly, it reduces token usage by utilizing shorter prompts, resulting in more efficient processing. Additionally, fine-tuning facilitates lower-latency requests, enabling faster and more responsive interactions...

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