Debugging Machine Learning Models with Python | Develop high-performance, low-bias, and explainable machine learning and deep learning models

Ali Madani

ISBN 10: 1800208588 / ISBN 13: 9781800208582

Verlag: Packt Publishing, 2023

Beschreibung

Debugging Machine Learning Models with Python | Develop high-performance, low-bias, and explainable machine learning and deep learning models | Ali Madani | Taschenbuch | Englisch | 2023 | Packt Publishing | EAN 9781800208582 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Bestandsnummer des Verkäufers 127662858

Bibliografische Details

• Titel: Debugging Machine Learning Models with Python | Develop high-performance, low-bias, and explainable machine learning and deep learning models
• Verlag: Packt Publishing
• Erscheinungsjahr: 2023
• Sprache: Englisch
• ISBN-10: 1800208588
• ISBN-13: 9781800208582
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 191 H x 19 T Millimeter Breite: 235 mm, Höhe: 191 mm, Tiefe: 19 mm
• Gewicht: 643 Gramm
• Verkäuferkataloge:
  • Bücher

Über diesen Titel

Inhaltsangabe

Master reproducible ML and DL models with Python and PyTorch to achieve high performance, explainability, and real-world success

Key Features

• Learn how to improve performance of your models and eliminate model biases
• Strategically design your machine learning systems to minimize chances of failure in production
• Discover advanced techniques to solve real-world challenges
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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 like healthcare, finance, and e-commerce.

What you will learn

• Enhance data quality and eliminate data flaws
• Effectively assess and improve performance of your models
• Optimize deep learning models built in PyTorch
• Mitigate biases to ensure fairness
• Understand explainability techniques to improve model qualities
• Use test-driven modeling for data processing and modelling improvement
• Explore techniques to bring reliable models to production
• Discover the benefits of causal and human-in-the-loop modeling

Who This Book Is For

This book is for data scientists, analysts, machine learning engineers, Python developers, and students looking to build reliable, high-performance, and explainable machine learning models for production across diverse industrial applications. Fundamental Python skills are all you need to dive into the concepts and practical examples covered. Whether you're new to machine learning or an experienced practitioner, this book offers a breadth of knowledge and practical insights to elevate your modeling skills.

Table of Contents

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

Über die Autorin bzw. den Autor

Ali Madani worked as the director of machine learning at Cyclica Inc leading AI technology development front of Cyclica for drug discovery before acquisition of Cyclica by Recursion Pharmaceuticals. Ali completed his PhD at University of Toronto focusing on machine learning modeling in cancer setting and attained a Master of Mathematics from the University of Waterloo. As a believer in industry-oriented education and pro-democratization of knowledge, Ali has actively educated students and professionals through international workshops and courses on basic and advanced high-quality machine learning modeling. When not immersed in machine learning modeling and teaching, Ali enjoys exercising, cooking and traveling with his partner.