Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Interpretable Machine Learning, 2nd Edition: A Guide for Making Black Box Models Explainable
Top Free Computer Networking Books 🌠 - 100% Free or Open Source!
  • Title: Interpretable Machine Learning, 2nd Edition: A Guide for Making Black Box Models Explainable
  • Author(s) Christoph Molnar
  • Publisher: Independently published (2022); eBook (2nd Edition, 2024-05-26; Creative Commons Licensed)
  • License(s): Creative Commons License (CC)
  • Paperback: 329 pages
  • eBook: HTML
  • Language: English
  • ISBN-10/ASIN: B09TMWHVB4
  • ISBN-13: 979-8411463330
  • Share This:  

Book Description

TThis book covers a range of interpretability methods, from inherently interpretable models to methods that can make any model interpretable, such as SHAP, LIME and permutation feature importance.

About the Authors
  • Christoph Molnar, is an expert in machine learning and statistics, with a Ph.D. in interpretable machine learning from Ludwig-Maximilians Universität München, LocationMunich Area, Germany.
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • Fairness and Machine Learning: Limitations and Opportunities

    This book is an introduction to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making.

  • An Introduction to Machine Learning Interpretability

    Understanding and trusting models and their results is a hallmark of good science. Get an applied perspective on how this applies to machine learning, including fairness, accountability, transparency, and explainable AI.

  • Machine Learning Yearning (Andrew Ng)

    You will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.

  • The Hundred-Page Machine Learning Book (Andriy Burkov)

    Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.

  • Foundations of Machine Learning (Mehryar Mohri, et al)

    This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

  • Dive into Deep Learning (Aston Zhang, et al.)

    This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.

  • Understanding Machine Learning: From Theory to Algorithms

    Explains the principles behind the automated learning approach and the considerations underlying its usage. Provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations.

  • Automated Machine Learning: Methods, Systems, Challenges

    This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems.

  • Reinforcement Learning: An Introduction, Second Edition

    It provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.

  • Probabilistic Machine Learning: An Introduction (Kevin Murphy)

    This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

Book Categories
:
Other Categories
Resources and Links