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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

Introduction

So far, you’ve been using the knowledge you were taught at the beginning of the book about GitHub Copilot and ChatGPT. This foundational knowledge was enough to teach you how to write prompts and accept them. It was also enough to let you start work on creating solutions for machine learning, data science, and web development. In the case of web development, you also discovered Copilot is an efficient tool when working with existing code bases. In this chapter, we want to take your AI tool knowledge to the next level, as there are more features that you may want to leverage.

There are a lot of things that can be done to increase efficiency; you will see later in the chapter how there are features within Copilot that let you scaffold files, and you will learn more about your workspace and even Visual Studio Code as an editor, which are all time-saving features. This chapter will cover some of the most important features.

In this chapter, we will:

    ...

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