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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

Problem and data domain

In this context, we will utilize CNNs to tackle the object recognition task using the CIFAR-10 dataset. CNNs are particularly effective for image-related problems due to their ability to automatically learn hierarchical features from raw pixel data. By training a CNN model on the CIFAR-10 dataset, we aim to develop a robust system capable of accurately classifying objects into one of the ten predefined categories. This model can be applied in various domains, such as image-based search engines, automated surveillance systems, and quality control in manufacturing.

Dataset overview

The CIFAR-10 dataset comprises 60,000 color images, divided into 10 classes, with 6,000 images per class. Each image has dimensions of 32x32 pixels and is represented in RGB format. The dataset is split into a training set of 50,000 images and a test set of 10,000 images.

Features in the dataset include:

  • Image data: Color images of various objects, each represented...

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