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    AI-Assisted Programming for Web and Machine Learning. Improve your development workflow with ChatGPT and GitHub Copilot Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar

    (ebook) (audiobook) (audiobook) Książka w języku 1
    AI-Assisted Programming for Web and Machine Learning. Improve your development workflow with ChatGPT and GitHub Copilot Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar - okladka książki

    AI-Assisted Programming for Web and Machine Learning. Improve your development workflow with ChatGPT and GitHub Copilot Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar - okladka książki

    AI-Assisted Programming for Web and Machine Learning. Improve your development workflow with ChatGPT and GitHub Copilot Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar - audiobook MP3

    AI-Assisted Programming for Web and Machine Learning. Improve your development workflow with ChatGPT and GitHub Copilot Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar - audiobook CD

    Autorzy:
    Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar
    Serie wydawnicze:
    Expert Insight
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    602
    Dostępne formaty:
         PDF
         ePub
    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.

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    O autorach książki

    Christoffer Noring is a software developer with more than 10 years of experience. He has successfully delivered software for different industries, ranging from telecom to aviation. Throughout his career, he has worked on everything, right from databases to frontends. He is very passionate about community and sharing knowledge, which is why he frequently speaks on topics ranging from TDD, React, and NativeScript to Angular. He also writes books and blogs frequently.

    He holds the title of Google Developer Expert in web technologies and AngularJS/Angular. He is also a Telerik Developer Expert in the mobile framework NativeScript. Christoffer currently works for McKinsey as a fullstack developer. He is the author and maintainer of the book RxJS Ultimate, which aims to be a free resource to help the community.
    Anjali Jain is a London-based AI and ML professional with a career spanning over two decades. Currently working as a data architect for Metrobank, she brings her expertise in AI, data, architecture, data governance, and software development to the financial sector. Anjali holds a bachelor’s degree in electrical engineering and boasts certifications, including TOGAF 9.1 and ITIL 2011 Foundation. In her role as Senior AI and ML tutor at Oxford, she shares cutting-edge knowledge on various technologies.
    Marina Fernandez is a data science and Databricks consultant with expertise in financial risk management. She contributes to the academic team at the University of Oxford, where she holds the positions of senior AI and ML tutor and guest lecturer.
    Ajit Jaokar is a data scientist for Feynlabs, building AI prototypes for complex applications. He is also a course director for artificial intelligence at the University of Oxford. Besides this, Ajit is a visiting fellow in Engineering Sciences at the University of Oxford and conducts AI courses at the London School of Economics, Universidad Politécnica de Madrid, and the Harvard Kennedy School of Government as part of The Future Society.
    His work at Oxford and his company is based on interdisciplinary aspects of artificial intelligence, including AI with digital twins, quantum computing, metaverse, Agtech, and life sciences. His teaching is based on a methodology for AI and cyber-physical systems, which he is developing as part of his research.

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