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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

In the previous chapter, we gave some historical context to how AI has developed over the years, how we’ve gone from natural language processing (NLP) to large language models (LLMs), and how the latter serves as the underlying machine learning model in AI assistants. To use these AI assistants, you use natural language prompts as input. However, to ensure you “prompt” in an efficient way, so that you get what you want, it’s important to have a strategy, and that’s what this chapter aims to give you.

How to “prompt” efficiently is commonly known in the industry as a “prompt strategy” or “prompt engineering.” It’s not an engineering practice in the common sense of the word but rather an art form where practitioners of AI assistants have discovered patterns and practices that seem to work well. We, the authors of this book, are building upon those discovered practices and aim to describe...

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