| 书目名称 | Explainable AI with Python | | 编辑 | Leonida Gianfagna,Antonio Di Cecco | | 视频video | | | 概述 | Offers a high-level perspective that explains the basics of XAI and its impacts on business and society, as well as a useful guide for machine learning practitioners to understand the current techniqu | | 图书封面 |  | | 描述 | .This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches presented can be applied to almost all the current “machine learning” models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others..Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. .Explainable AI with Python. fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI..Beginning with examples of what Explainable AI (XAI) is and why it is needed in the field, the book details different approaches to XAI depending on specific context and need. Hands-on work on interpretable models with specific examples leveraging Python are then presented, | | 出版日期 | Book 2021 | | 关键词 | XAI; Artificial Intelligence; Machine Learning; intrinsic interpretable models; Shapley Values; Deep Tayl | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-68640-6 | | isbn_softcover | 978-3-030-68639-0 | | isbn_ebook | 978-3-030-68640-6 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
The information of publication is updating
|
|
发表于 2025-3-21 16:37:35
