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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

Prompt strategy

The prompts we are about to use provide high-level guidance for Copilot, and the outputs/results allow further tailoring of Copilot’s responses to match the specific dataset and analysis needs.

The key aspects of the prompting approach are:

  • Define the task. Clearly instruct the AI assistant what task we are solving.
  • Break down into steps. Breaking the data exploration down into logical steps (like data loading, inspection, summary stats etc.)
  • Providing context/intent for each prompt to guide Copilot (like requesting numeric summary statistics)
  • Sharing previous results as input. Sharing outputs and results from Copilot’s code snippets to further guide the conversation (like printing the summary stats)
  • Refine, iteratively refining prompts and conversing with Copilot in a back-and-forth way

Therefore, we will use the TAG (Task-Action-Guidance) prompt pattern described in Chapter 2. Let’s describe this...

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