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    The Regularization Cookbook. Explore practical recipes to improve the functionality of your ML models Vincent Vandenbussche, Akin Osman Kazakci

    (ebook) (audiobook) (audiobook) Książka w języku angielskim
    The Regularization Cookbook. Explore practical recipes to improve the functionality of your ML models Vincent Vandenbussche, Akin Osman Kazakci - okladka książki

    The Regularization Cookbook. Explore practical recipes to improve the functionality of your ML models Vincent Vandenbussche, Akin Osman Kazakci - okladka książki

    The Regularization Cookbook. Explore practical recipes to improve the functionality of your ML models Vincent Vandenbussche, Akin Osman Kazakci - audiobook MP3

    The Regularization Cookbook. Explore practical recipes to improve the functionality of your ML models Vincent Vandenbussche, Akin Osman Kazakci - audiobook CD

    Autorzy:
    Vincent Vandenbussche, Akin Osman Kazakci
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    424
    Dostępne formaty:
         PDF
         ePub
    Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working codes as well as theoretical explanations.

    After an introduction to regularization and methods to diagnose when to use it, you’ll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. You’ll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. In the last five chapters, you’ll discover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, you’ll dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. By the end, you’ll explore regularization for computer vision, covering CNN specifics, along with the use of generative models such as stable diffusion and Dall-E.

    By the end of this book, you’ll be armed with different regularization techniques to apply to your ML and DL models.

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    O autorze książki

    After a Ph.D. in Physics, Vincent Vandenbussche has worked for a decade in the industry, deploying ML solutions at scale. He has worked in numerous companies, such as Renault, L’Oréal, General Electric, Jellysmack, Chanel, and CERN.
    He also has a passion for teaching: he co-founded a data science bootcamp, was an ML lecturer at Mines Paris engineering school and EDHEC business school and trained numerous professionals in companies like ArcelorMittal and Orange.

    Packt Publishing - inne książki

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