| 書目名稱 | Practical Explainable AI Using Python | | 副標題 | Artificial Intellige | | 編輯 | Pradeepta Mishra | | 視頻video | http://file.papertrans.cn/753/752971/752971.mp4 | | 概述 | Covers the core features of explainability and how to execute them using Python frameworks.Explains XAI features to interpret supervised learning algorithms, NLP components and deep learning neural ne | | 圖書封面 |  | | 描述 | Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers..You‘ll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you‘ll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision.Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, youwill be introduced to model explainability for unstructured data,?classification problems,?and natural language processing–related tasks. Additionally, the book looks at counterfactual explanations for AI models. .Practical Explainable AI Using Python. shine | | 出版日期 | Book 2022 | | 關(guān)鍵詞 | Explainable Artificial Intelligence; Interpretable Artificial Intelligence; Python; Interpret the Black | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-7158-2 | | isbn_softcover | 978-1-4842-7157-5 | | isbn_ebook | 978-1-4842-7158-2 | | copyright | Pradeepta Mishra 2022 |
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