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AI-Assisted Programming for Web and Machine Learning

AI-Assisted Programming for Web and Machine Learning

By : Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar
4.9 (11)
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AI-Assisted Programming for Web and Machine Learning

AI-Assisted Programming for Web and Machine Learning

4.9 (11)
By: Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar

Overview of this book

AI-Assisted Programming for Web and Machine Learning shows you how to build applications and machine learning models and automate repetitive tasks. Part 1 focuses on coding, from building a user interface to the backend. You’ll use prompts to create the appearance of an app using HTML, styling with CSS, adding behavior with JavaScript, and working with multiple viewports. Next, you’ll build a web API with Python and Flask and refactor the code to improve code readability. Part 1 ends with using GitHub Copilot to improve the maintainability and performance of existing code. Part 2 provides a prompting toolkit for data science from data checking (inspecting data and creating distribution graphs and correlation matrices) to building and optimizing a neural network. You’ll use different prompt strategies for data preprocessing, feature engineering, model selection, training, hyperparameter optimization, and model evaluation for various machine learning models and use cases. The book closes with chapters on advanced techniques on GitHub Copilot and software agents. There are tips on code generation, debugging, and troubleshooting code. You’ll see how simpler and AI-powered agents work and discover tool calling.
Table of Contents (25 chapters)
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3
Tools of the Trade: Introducing Our AI Assistants
23
Other Books You May Enjoy
24
Index

Summary

Throughout this chapter, we’ve provided a strategy for solving problems with prompts and validating the solution.

You’ve seen how both web development and data science can be broken down into smaller parts that can be solved with prompts. We also identified some basic principles for writing prompts.

Finally, we looked at how to validate the solution using prompts and classical verification techniques.

It’s our hope that you will revisit this chapter when you’re looking at solving a problem within web development or data science and you’re looking for an approach.

There’s more to prompting than writing a prompt and getting a response. You will see throughout this book how we use these principles in various domains to solve problems. Try typing these prompts as you read, adapt to your own needs, and see what happens.

In the next chapter, we are going to learn more about the two AI assistants of our choice, GitHub...

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