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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

Building upon our foundational understanding of predictive modeling, we now dive into the dynamic world of Multilayer Perceptron (MLP) models. In this chapter, we embark on a journey to construct an MLP model from scratch, leveraging the versatility and power of neural networks for predictive analytics.

Our exploration of MLPs represents a significant leap into the realm of complex modeling techniques. While linear regression provided valuable insights into modeling relationships within data, MLPs offer a rich framework for capturing intricate patterns and nonlinear dependencies, making them well suited for a wide range of predictive tasks.

Through hands-on experimentation and iterative refinement, we will unravel the intricacies of MLP architecture and optimization. From designing the initial network structure to fine-tuning hyperparameters and incorporating advanced techniques such as batch normalization and dropout, we aim to equip you with the knowledge and...

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