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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

Breaking the problem down into features

Given the CIFAR-10 dataset and the application of CNNs for image recognition, we outline the following features to guide users through building and optimizing CNN models:

  • Building the baseline CNN model with a single convolutional layer: Users will start by constructing a simple CNN model with a single convolutional layer for image classification. This feature focuses on defining the basic architecture, including convolutional filters, activation functions, and pooling layers, to establish a foundational understanding of CNNs.
  • Experimenting with the addition of convolutional layers: Users will explore the impact of adding additional convolutional layers to the baseline model architecture. By incrementally increasing the depth of the network, users can observe how the model’s capacity to capture hierarchical features evolves and its ability to learn complex patterns improves.
  • Incorporating dropout regularization...

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