Description: Explainable AI with Python Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Leonida Gianfagna, Antonio Di Cecco Format: Paperback Publisher: Springer Nature Switzerland AG, Switzerland Imprint: Springer Nature Switzerland AG ISBN-13: 9783030686390, 978-3030686390 Synopsis 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, showing how intrinsic interpretable models can be interpreted and how to produce "human understandable" explanations. Model-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are "opaque." Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for the future. Taking a practical perspective, the authors demonstrate how to effectively use ML and XAI in science. The final chapter explains Adversarial Machine Learning and how to do XAI with adversarial examples.
Price: 43.6 GBP
Location: Aldershot
End Time: 2024-02-29T09:07:10.000Z
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Book Title: Explainable AI with Python
Item Height: 235mm
Item Width: 155mm
Author: Leonida Gianfagna, Antonio Di Cecco
Publication Name: Explainable Ai with Python
Format: Paperback
Language: English
Publisher: Springer Nature Switzerland A&G
Subject: Computer Science
Publication Year: 2021
Type: Textbook
Item Weight: 332g
Number of Pages: 202 Pages
