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Debugging machine learning models with Python : develop high-performance, low-bias, and explainable machine learning and deep learning models /

Debugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-perfor...

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Bibliographic Details
Call Number:Libro Electrónico
Main Author: Madani, Ali (Author)
Other Authors: MacKinnon, Stephen (writer of foreword.)
Format: Electronic eBook
Language:Inglés
Published: Birmingham, UK : Packt Publishing Ltd., 2023.
Subjects:
Online Access:Texto completo (Requiere registro previo con correo institucional)
Description
Summary:Debugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-performance models for industrial applications. Whether you're a data scientist, analyst, machine learning engineer, or Python developer, this book will empower you to design modular systems for data preparation, accurately train and test models, and seamlessly integrate them into larger technologies. By bridging the gap between theory and practice, you'll learn how to evaluate model performance, identify and address issues, and harness recent advancements in deep learning and generative modeling using PyTorch and scikit-learn. Your journey to developing high quality models in practice will also encompass causal and human-in-the-loop modeling and machine learning explainability. With hands-on examples and clear explanations, you'll develop the skills to deliver impactful solutions across domains such as healthcare, finance, and e-commerce.
Item Description:Includes index.
Physical Description:1 online resource : illustrations
ISBN:9781800201132
1800201133