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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

Reflection on prompts for this use case

Just like with our first use case in this chapter, we followed a specific method of first setting high-level context of describing our role, the shape of the dataset, and what we were looking to do. Then, we followed the below process to first breaking down the problem in steps, getting code and how we could continue to improve and refine and finally visualize the results:

  1. Step-by-step guidance: The requests are structured as a series of step-by-step tasks, breaking down the larger problem into manageable components. This makes it easier to follow and implement the solution incrementally.
  2. Specify inputs and requirements: Provide clear and specific information about the dataset, its columns, and the requirements for preprocessing, clustering, and visualization. This helps ensure that the assistance received is tailored to the particular needs.
  3. Request for code with comments: Code snippets request to include comments...

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