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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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Unveiling the Black Box: Practical Deep Learning and Explainable AI
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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
Edité par
LAP LAMBERT Academic Publishing, 2024
ISBN 10 : 3659396702
ISBN 13 : 9783659396700
Neuf
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Unveiling the Black Box: Practical Deep Learning and Explainable AI
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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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ISBN 10 : 3659396702
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Unveiling the Black Box: Practical Deep Learning and Explainable AI
Edité par
LAP LAMBERT Academic Publishing Okt 2024, 2024
ISBN 10 : 3659396702
ISBN 13 : 9783659396700
Neuf
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Taschenbuch. Etat : Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 192 pp. Englisch. N° de réf. du vendeur 9783659396700
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Unveiling the Black Box (Paperback)
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Paperback. Etat : 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. N° de réf. du vendeur 9783659396700
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Unveiling the Black Box (Paperback)
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ISBN 10 : 3659396702
ISBN 13 : 9783659396700
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Paperback. Etat : 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. N° de réf. du vendeur 9783659396700
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Unveiling the Black Box (Paperback)
Edité par
LAP Lambert Academic Publishing, 2024
ISBN 10 : 3659396702
ISBN 13 : 9783659396700
Neuf
Paperback
Vendeur : Grand Eagle Retail, Mason, OH, Etats-Unis
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Paperback. Etat : 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. N° de réf. du vendeur 9783659396700
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