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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

Summary

In this chapter, we explored how to effectively use AI assistants like ChatGPT to learn and experiment with convolutional neural network (CNN) models. The strategies provided a clear step-by-step approach to experimenting with different techniques for building and training CNN models using the CIFAR-10 dataset.

Each step was accompanied by detailed instructions, code generation, and user validation, ensuring a structured learning experience. We started by building a baseline CNN model, where we learned the essential preprocessing steps, including normalizing pixel values and resizing images. It guided you through generating beginner-friendly code that is compatible with Jupyter notebooks, ensuring that even those new to the field could easily grasp the fundamentals of CNN construction.

As we progressed, our AI assistant became an integral part of the learning process, helping us delve into more complex areas such as adding layers, implementing dropout and batch normalization...

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