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

English | 2023 | ISBN: 978-1800208582 | 344 Pages | PDF, EPUB | 43 MB

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

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.

What you will learn

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