Isbn: 9783030686390 - explainable ai with python (16 resultados)

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    Editorial: Springer, 2021

    3030686396 / 9783030686390

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    paperback. Condición: Very Good. Cover and edges may have some wear.

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  • Idioma: Inglés

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  • Idioma: Inglés

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  • Idioma: Inglés

    Editorial: Springer Nature Switzerland AG, CH, 2021

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    Paperback. Condición: New. 1st ed. 2021. 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.…

  • Idioma: Inglés

    Editorial: Springer, 2021

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  • Idioma: Inglés

    Editorial: Springer 2021-05-31, 2021

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    Paperback. Condición: New.

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    Editorial: Springer, 2021

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    Condición: New. In English.

  • Idioma: Inglés

    Editorial: Springer, 2021

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    Editorial: Springer-Nature New York Inc, 2021

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    Paperback. Condición: Brand New. 210 pages. 9.25x6.10x0.45 inches. In Stock.

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  • Idioma: Inglés

    Editorial: Springer International Publishing, 2021

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    Condición: New. 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.

  • Idioma: Inglés

    Editorial: Springer, Berlin, Springer International Publishing, Springer, 2021

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    Taschenbuch. Condición: Neu. Neuware - 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.…

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    Editorial: Springer Nature Switzerland AG, CH, 2021

    3030686396 / 9783030686390

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    Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK

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    Paperback. Condición: New. 1st ed. 2021. 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.…

  • Idioma: Inglés

    Editorial: Springer, 2021

    3030686396 / 9783030686390

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    Taschenbuch. Condición: Neu. Explainable AI with Python | Leonida Gianfagna (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2021 | Springer | EAN 9783030686390 | Verantwortliche Person für die EU: Springer Heidelberg, Tiergartenstr. 17, 69121 Heidelberg, buchhandel-buch[at]springer[dot]com | Anbieter: preigu. …