Madani A. - Debugging Machine Learning Models with Python [2023, EPUB, ENG] torrent

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Debugging Machine Learning Models with Python

Год издания: 2023
Автор: Madani A.

Издательство: Packt
ISBN: 9781800208582
Язык: Английский

Формат: EPUB
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 344

Описание: 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.
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Preface
Part 1:Debugging for Machine Learning Modeling
Chapter 1: Beyond Code Debugging
Chapter 2: Machine Learning Life Cycle
Chapter 3: Debugging toward Responsible AI
Part 2:Improving Machine Learning Models
Chapter 4: Detecting Performance and Efficiency Issues in Machine Learning Models
Chapter 5: Improving the Performance of Machine Learning Models
Chapter 6: Interpretability and Explainability in Machine Learning Modeling
Chapter 7: Decreasing Bias and Achieving Fairness
Part 3:Low-Bug Machine Learning Development and Deployment
Chapter 8: Controlling Risks Using Test-Driven Development
Chapter 9: Testing and Debugging for Production
Chapter 10: Versioning and Reproducible Machine Learning Modeling
Chapter 11: Avoiding and Detecting Data and Concept Drifts
Part 4:Deep Learning Modeling
Chapter 12: Going Beyond ML Debugging with Deep Learning
Chapter 13: Advanced Deep Learning Techniques
Chapter 14: Introduction to Recent Advancements in Machine Learning
Part 5:Advanced Topics in Model Debugging
Chapter 15: Correlation versus Causality
Chapter 16: Security and Privacy in Machine Learning
Chapter 17: Human-in-the-Loop Machine Learning
Assessments
Index
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