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Titlebook: Explainable AI Recipes; Implement Solutions Pradeepta Mishra Book 2023 Pradeepta Mishra 2023 Explainable AI.Python.Artificial Intelligence

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發(fā)表于 2025-3-21 16:07:48 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Explainable AI Recipes
副標(biāo)題Implement Solutions
編輯Pradeepta Mishra
視頻videohttp://file.papertrans.cn/320/319278/319278.mp4
概述Explains the core features of XAI and how to execute them using Python frameworks.Covers interpreting supervised learning algorithms and single instance predictions with XAI.Includes best practices fo
圖書封面Titlebook: Explainable AI Recipes; Implement Solutions  Pradeepta Mishra Book 2023 Pradeepta Mishra 2023 Explainable AI.Python.Artificial Intelligence
描述Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms.?.The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution,and activation attribution.? ?.After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses..What Y
出版日期Book 2023
關(guān)鍵詞Explainable AI; Python; Artificial Intelligence; Linaer Supervised Model; Non Linear Supervised Model; En
版次1
doihttps://doi.org/10.1007/978-1-4842-9029-3
isbn_softcover978-1-4842-9028-6
isbn_ebook978-1-4842-9029-3
copyrightPradeepta Mishra 2023
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發(fā)表于 2025-3-21 21:20:53 | 只看該作者
Explainability for Deep Learning Models,uch as audio processing, text classification, etc.; deep neural networks, which are used for building extremely deep networks; and finally, convolutional neural network models, which are used for image classification.
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發(fā)表于 2025-3-22 01:58:47 | 只看該作者
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發(fā)表于 2025-3-22 12:22:36 | 只看該作者
https://doi.org/10.1007/978-3-030-19175-7 number of features for a machine learning task increases or the volume of data increases, then it takes a lot of time to apply machine learning techniques. That’s when deep learning techniques are used.
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發(fā)表于 2025-3-22 13:13:59 | 只看該作者
Introducing Explainability and Setting Up Your Development Environment, number of features for a machine learning task increases or the volume of data increases, then it takes a lot of time to apply machine learning techniques. That’s when deep learning techniques are used.
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發(fā)表于 2025-3-22 21:55:41 | 只看該作者
Mirjana Pavlovic,John Mayfield,Bela Balint business to plan better and will help decision-makers to plan according to the future estimations. There are machine learning–based techniques that can be applied to generate future forecasting; also, there is a need to explain the predictions about the future.
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