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AI-Assisted Programming for Web and Machine Learning

You're reading from   AI-Assisted Programming for Web and Machine Learning Improve your development workflow with ChatGPT and GitHub Copilot

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835086056
Length 602 pages
Edition 1st Edition
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Authors (5):
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Marina Fernandez Marina Fernandez
Author Profile Icon Marina Fernandez
Marina Fernandez
Ajit Jaokar Ajit Jaokar
Author Profile Icon Ajit Jaokar
Ajit Jaokar
Anjali Jain Anjali Jain
Author Profile Icon Anjali Jain
Anjali Jain
Christoffer Noring Christoffer Noring
Author Profile Icon Christoffer Noring
Christoffer Noring
Ayşe Mutlu Ayşe Mutlu
Author Profile Icon Ayşe Mutlu
Ayşe Mutlu
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Toc

Table of Contents (25) Chapters Close

Preface 1. It’s a New World, One with AI Assistants, and You’re Invited 2. Prompt Strategy FREE CHAPTER 3. Tools of the Trade: Introducing Our AI Assistants 4. Build the Appearance of Our App with HTML and Copilot 5. Style the App with CSS and Copilot 6. Add Behavior with JavaScript 7. Support Multiple Viewports Using Responsive Web Layouts 8. Build a Backend with Web APIs 9. Augment Web Apps with AI Services 10. Maintaining Existing Codebases 11. Data Exploration with ChatGPT 12. Building a Classification Model with ChatGPT 13. Building a Regression Model for Customer Spend with ChatGPT 14. Building an MLP Model for Fashion-MNIST with ChatGPT 15. Building a CNN Model for CIFAR-10 with ChatGPT 16. Unsupervised Learning: Clustering and PCA 17. Machine Learning with Copilot 18. Regression with Copilot Chat 19. Regression with Copilot Suggestions 20. Increasing Efficiency with GitHub Copilot 21. Agents in Software Development 22. Conclusion 23. Other Books You May Enjoy
24. Index

Validate the solution

The most important part of this strategy is verifying correctness and that the text and code created by the AI assistant are correct. There are two general approaches we can take to verify our outcome:

  • Verification via prompts: The first approach is to use prompts to verify the outcome. This means writing prompts that question the outcome of specific results. This can be a good strategy to employ at the beginning of your verification process. What you’re looking for are situations where the AI assistant isn’t consistent in its responses.
  • Classical verifications: The second approach is to use classical verification techniques. What those techniques are varies depending on the problem domain. At a high level, though, it boils down to testing code, comparing output, and relying on your own knowledge, and the knowledge of your peers, to verify the outcome.

The AI tool doesn’t really know what it’s doing. The...

You have been reading a chapter from
AI-Assisted Programming for Web and Machine Learning
Published in: Aug 2024
Publisher: Packt
ISBN-13: 9781835086056
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