000			03838nam a2200313uu 4500
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024	8	0	_a9781835083895
040			_aPACKT _cPACKT
041			_aen
044			_aGB
100	0		_aChristoffer Noring _eauthor.
700	0		_aAnjali Jain _eauthor.
700	0		_aMarina Fernandez _eauthor.
700	0		_aAyşe Mutlu _eauthor.
700	0		_aAjit Jaokar _eauthor.
710	2		_aPACKT
773	0		_tAI-Assisted Programming for Web and Machine Learning _dGB,Packt,2024-08-30 _h602
245	0	0	_aAI-Assisted Programming for Web and Machine Learning.
300			_a602.
377			_aen
260			_aGB: _bPackt, _c2024-08-30.
263			_a2024-08-30
264		1	_aGB: _bPackt,
520			_a<p><b>Speed up your development processes and improve your productivity by writing practical and relevant prompts to build web applications and Machine Learning (ML) models Purchase of the print or Kindle book includes a free PDF copy</b></p><h4>Key Features</h4><ul><li>Utilize prompts to enhance frontend and backend web development</li><li>Develop prompt strategies to build robust machine learning models</li><li>Use GitHub Copilot for data exploration, maintaining existing code bases, and augmenting ML models into web applications</li></ul><h4>Book Description</h4>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.<h4>What you will learn</h4><ul><li>Speed up your coding and machine learning workflows with GitHub Copilot and ChatGPT</li><li>Use an AI-assisted approach across the development lifecycle </li><li>Implement prompt engineering techniques in the data science lifecycle</li><li>Develop the frontend and backend of a web application with AI assistance </li><li>Build machine learning models with GitHub Copilot and ChatGPT </li><li>Refactor code and fix faults for better efficiency and readability </li><li>Improve your codebase with rich documentation and enhanced workflows </li></ul><h4>Who this book is for</h4>Experienced developers new to GitHub Copilot and ChatGPT can discover the best strategies to improve productivity and deliver projects quicker than traditional methods. This book is ideal for software engineers working on web or machine learning projects. It is also a useful resource for web developers, data scientists, and analysts who want to improve their efficiency with the help of prompting. This book does not teach web development or how different machine learning models work.
538			_aData in extended ASCII character set.
538			_aMode of access: Internet.
856	4	0	_uhttps://learning.packt.com/product/476200
999			_c16006 _d16006