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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

Machine learning, or ML, involves data and learning patterns from that said data and using those patterns to make predictions or decisions. Machine learning consists of a series of steps, all the way from loading data and cleaning it to eventually training a model to get the insights you need from said model. All these steps are roughly the same for most problems in this problem space. However, details may differ, like the choice of pre-processing step, the choice of algorithm, etc. An AI tool like GitHub Copilot comes into machine learning from a few different angles:

  • Suggesting workflows: Thanks to Copilot having been trained in machine learning work flows, it’s able to suggest a workflow that fits your problem.
  • Recommending tools and algorithms: If you provide your AI tool with enough context on what your problem is and the shape of your data, an AI tool like Copilot can suggest tools and algorithms that fit your specific problem.
  • Code...

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