Explainable AI with Python

Leonida Gianfagna|Antonio Di Cecco

ISBN 10: 3030686396 / ISBN 13: 9783030686390

Editore: Springer International Publishing, 2021

Riguardo questo articolo

Offers a&nbsphigh-level perspective that explains the basics of XAI and its impacts on business and society, as well as&nbsp a useful guide for machine learning practitioners to understand the current techniques to achieve explainability for AIML systems.

Codice articolo 458552508

Dati bibliografici

• Titolo: Explainable AI with Python
• Editore: Springer International Publishing
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 3030686396
• ISBN 13: 9783030686390
• Rilegatura: Brossura
• Condizione: New
• Cataloghi dei venditori:
  • Mathematik/Naturwissenschaften/Technik/Medizin

Descrizione articolo

Riassunto

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.

Informazioni sull'autore

Leonida Gianfagna (Phd, MBA) is a theoretical physicist that is currently working in Cyber Security as R&D director for Cyber Guru. Before joining Cyber Guru he worked in IBM for 15 years covering leading roles in software development in ITSM (IT Service Management). He is the author of several publications in theoretical physics and computer science and accredited as IBM Master Inventor (15+ filings). 

Antonio Di Cecco is a theoretical physicist with a strong mathematical background that is fully engaged on delivering education on AIML at different levels from dummies to experts (face to face classes and remotely). The main strength of his approach is the deep-diving of the mathematical foundations of AIML models that open new angles to present the AIML knowledge and space of improvements for the existing state of art. Antonio has also a “Master in Economics” with focus innovation and teaching experiences. He is leading School of AI in Italy with chapters in Rome and Pescara