Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Unveiling the Black Box: Practical Deep Learning and Explainable AI

Unveiling the Black Box: Practical Deep Learning and Explainable AI

Roy Chowdhury, Tathagata
Dey, Sudipta

Paperback

Technology & Engineering

ISBN10: 3659396702
ISBN13: 9783659396700
Publisher: LAP Lambert Academic Publishing
Published: Oct 28 2024
Pages: 192
Weight: 0.64
Height: 0.44 Width: 6.00 Depth: 9.00
Language: English
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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