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Synopsis
Unveiling the Black Box: Practical Deep Learning and Explainable AI" offers a comprehensive overview of Explainable AI (XAI) techniques and their significance in ensuring transparency and trust in complex AI models. With AI applications spanning healthcare, finance, and autonomous systems, the opacity of deep learning models often raises ethical, legal, and reliability concerns. This guide explores foundational AI model structures, such as Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), highlighting their architecture, functionality, and real-world applications. To enhance interpretability, the text introduces leading XAI methods like Local Interpretable Model-Agnostic Explanations (LIME) and SHAPley Additive Explanations (SHAP), which enable users to understand model predictions. Advanced techniques, including Transfer Learning and Attention Mechanisms, are discussed to illustrate their impact on neural network adaptability and performance. The challenges of achieving interpretable AI, such as managing bias, balancing accuracy, and ensuring privacy, are also addressed.
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- PublisherLAP LAMBERT Academic Publishing
- Publication date2024
- ISBN 10 3659396702
- ISBN 13 9783659396700
- BindingPaperback
- LanguageEnglish
- Number of pages192
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Unveiling the Black Box: Practical Deep Learning and Explainable AI
Published by
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 3659396702
ISBN 13: 9783659396700
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Unveiling the Black Box (Paperback)
Published by
LAP Lambert Academic Publishing, 2024
ISBN 10: 3659396702
ISBN 13: 9783659396700
New
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Paperback. Condition: new. Paperback. Unveiling the Black Box: Practical Deep Learning and Explainable AI" offers a comprehensive overview of Explainable AI (XAI) techniques and their significance in ensuring transparency and trust in complex AI models. With AI applications spanning healthcare, finance, and autonomous systems, the opacity of deep learning models often raises ethical, legal, and reliability concerns. This guide explores foundational AI model structures, such as Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), highlighting their architecture, functionality, and real-world applications. To enhance interpretability, the text introduces leading XAI methods like Local Interpretable Model-Agnostic Explanations (LIME) and SHAPley Additive Explanations (SHAP), which enable users to understand model predictions. Advanced techniques, including Transfer Learning and Attention Mechanisms, are discussed to illustrate their impact on neural network adaptability and performance. The challenges of achieving interpretable AI, such as managing bias, balancing accuracy, and ensuring privacy, are also addressed. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9783659396700
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Unveiling the Black Box: Practical Deep Learning and Explainable AI
Published by
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 3659396702
ISBN 13: 9783659396700
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Unveiling the Black Box: Practical Deep Learning and Explainable AI
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Unveiling the Black Box: Practical Deep Learning and Explainable AI
Published by
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 3659396702
ISBN 13: 9783659396700
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Unveiling the Black Box (Paperback)
Published by
LAP Lambert Academic Publishing, 2024
ISBN 10: 3659396702
ISBN 13: 9783659396700
New
Paperback
Seller: AussieBookSeller, Truganina, VIC, Australia
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Paperback. Condition: new. Paperback. Unveiling the Black Box: Practical Deep Learning and Explainable AI" offers a comprehensive overview of Explainable AI (XAI) techniques and their significance in ensuring transparency and trust in complex AI models. With AI applications spanning healthcare, finance, and autonomous systems, the opacity of deep learning models often raises ethical, legal, and reliability concerns. This guide explores foundational AI model structures, such as Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), highlighting their architecture, functionality, and real-world applications. To enhance interpretability, the text introduces leading XAI methods like Local Interpretable Model-Agnostic Explanations (LIME) and SHAPley Additive Explanations (SHAP), which enable users to understand model predictions. Advanced techniques, including Transfer Learning and Attention Mechanisms, are discussed to illustrate their impact on neural network adaptability and performance. The challenges of achieving interpretable AI, such as managing bias, balancing accuracy, and ensuring privacy, are also addressed. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9783659396700
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Unveiling the Black Box (Paperback)
Published by
LAP Lambert Academic Publishing, 2024
ISBN 10: 3659396702
ISBN 13: 9783659396700
New
Paperback
Seller: Grand Eagle Retail, Fairfield, OH, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. Unveiling the Black Box: Practical Deep Learning and Explainable AI" offers a comprehensive overview of Explainable AI (XAI) techniques and their significance in ensuring transparency and trust in complex AI models. With AI applications spanning healthcare, finance, and autonomous systems, the opacity of deep learning models often raises ethical, legal, and reliability concerns. This guide explores foundational AI model structures, such as Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), highlighting their architecture, functionality, and real-world applications. To enhance interpretability, the text introduces leading XAI methods like Local Interpretable Model-Agnostic Explanations (LIME) and SHAPley Additive Explanations (SHAP), which enable users to understand model predictions. Advanced techniques, including Transfer Learning and Attention Mechanisms, are discussed to illustrate their impact on neural network adaptability and performance. The challenges of achieving interpretable AI, such as managing bias, balancing accuracy, and ensuring privacy, are also addressed. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783659396700
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