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|a ABES
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|e AFNOR
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|a eng
|2 639-2
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|a Noring, Christoffer.
|4 aut.
|e Auteur
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| 245 |
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|a AI-Assisted Programming for Web and Machine Learning :
|b Improve your development workflow with ChatGPT and GitHub Copilot
|c Christoffer Noring, Anjali Jain, Marina Fernandez, [et autres].
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| 264 |
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|a Birmingham :
|b Packt Publishing.
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| 264 |
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|a Paris :
|b Cyberlibris,
|c 2024.
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|b txt
|2 rdacontent
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|a Couverture (https://static2.cyberlibris.com/books_upload/136pix/9781835083895.jpg).
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|a L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement
|e Cyberlibris
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| 520 |
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|a 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. Key Features: Utilize prompts to enhance frontend and backend web development ; Develop prompt strategies to build robust machine learning models ; Use GitHub Copilot for data exploration, maintaining existing code bases, and augmenting ML models into web applications. Book Description: 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. What you will learn: Speed up your coding and machine learning workflows with GitHub Copilot and ChatGPTUse an AI-assisted approach across the development lifecycle ; Implement prompt engineering techniques in the data science lifecycleDevelop the frontend and backend of a web application with AI assistance ; Build machine learning models with GitHub Copilot and ChatGPT ; Refactor code and fix faults for better efficiency and readability ; Improve your codebase with rich documentation and enhanced workflows. Who this book is for: 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
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| 700 |
1 |
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|a Jain, Anjali.
|4 aut.
|e Auteur
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| 700 |
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|a Fernandez, Marina.
|4 aut.
|e Auteur
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| 700 |
1 |
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|a Mutlu, Ayşe.
|4 aut.
|e Auteur
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| 700 |
1 |
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|0 (IdRef)119446774
|1 http://www.idref.fr/119446774/id
|a Jaokar, Ajit.
|4 aut.
|e Auteur
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| 856 |
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|q HTML
|u https://srvext.uco.fr/login?url=https://univ.scholarvox.com/book/88958258
|w Données éditeur
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