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

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835086056

Length 602 pages

Edition 1st Edition

Languages

Python

Tools

ChatGPT

Concepts

Artificial Intelligence

Authors (5):

Marina Fernandez

Ajit Jaokar

Anjali Jain

Christoffer Noring

Ayşe Mutlu

+1 more

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Table of Contents (25) Chapters

Preface

1. It’s a New World, One with AI Assistants, and You’re Invited FREE CHAPTER

2. Prompt Strategy

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

Problem and data domain

In this context, we will utilize CNNs to tackle the object recognition task using the CIFAR-10 dataset. CNNs are particularly effective for image-related problems due to their ability to automatically learn hierarchical features from raw pixel data. By training a CNN model on the CIFAR-10 dataset, we aim to develop a robust system capable of accurately classifying objects into one of the ten predefined categories. This model can be applied in various domains, such as image-based search engines, automated surveillance systems, and quality control in manufacturing.

Dataset overview

The CIFAR-10 dataset comprises 60,000 color images, divided into 10 classes, with 6,000 images per class. Each image has dimensions of 32x32 pixels and is represented in RGB format. The dataset is split into a training set of 50,000 images and a test set of 10,000 images.

Features in the dataset include: