{"product_id":"deep-learning-and-xai-techniques-for-anomaly-detection-integrate-the-theory-and-practice-of-deep-anomaly-explainability-paperback","title":"Deep Learning and XAI Techniques for Anomaly Detection: Integrate the theory and practice of deep anomaly explainability - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eCher Simon\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCreate interpretable AI models for transparent and explainable anomaly detection with this hands-on guide\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePurchase of the print or Kindle book includes a free PDF eBook\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eBuild auditable XAI models for replicability and regulatory compliance\u003c\/li\u003e\n\u003cli\u003eDerive critical insights from transparent anomaly detection models\u003c\/li\u003e\n\u003cli\u003eStrike the right balance between model accuracy and interpretability\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eDespite promising advances, the opaque nature of deep learning models makes it difficult to interpret them, which is a drawback in terms of their practical deployment and regulatory compliance.\u003c\/p\u003e\u003cp\u003eDeep Learning and XAI Techniques for Anomaly Detection shows you state-of-the-art methods that'll help you to understand and address these challenges. By leveraging the Explainable AI (XAI) and deep learning techniques described in this book, you'll discover how to successfully extract business-critical insights while ensuring fair and ethical analysis.\u003c\/p\u003e\u003cp\u003eThis practical guide will provide you with tools and best practices to achieve transparency and interpretability with deep learning models, ultimately establishing trust in your anomaly detection applications. Throughout the chapters, you'll get equipped with XAI and anomaly detection knowledge that'll enable you to embark on a series of real-world projects. Whether you are building computer vision, natural language processing, or time series models, you'll learn how to quantify and assess their explainability.\u003c\/p\u003e\u003cp\u003eBy the end of this deep learning book, you'll be able to build a variety of deep learning XAI models and perform validation to assess their explainability.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eExplore deep learning frameworks for anomaly detection\u003c\/li\u003e\n\u003cli\u003eMitigate bias to ensure unbiased and ethical analysis\u003c\/li\u003e\n\u003cli\u003eIncrease your privacy and regulatory compliance awareness\u003c\/li\u003e\n\u003cli\u003eBuild deep learning anomaly detectors in several domains\u003c\/li\u003e\n\u003cli\u003eCompare intrinsic and post hoc explainability methods\u003c\/li\u003e\n\u003cli\u003eExamine backpropagation and perturbation methods\u003c\/li\u003e\n\u003cli\u003eConduct model-agnostic and model-specific explainability techniques\u003c\/li\u003e\n\u003cli\u003eEvaluate the explainability of your deep learning models\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis book is for anyone who aspires to explore explainable deep learning anomaly detection, tenured data scientists or ML practitioners looking for Explainable AI (XAI) best practices, or business leaders looking to make decisions on trade-off between performance and interpretability of anomaly detection applications. A basic understanding of deep learning and anomaly detection-related topics using Python is recommended to get the most out of this book.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 218\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.46 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e January 31, 2023\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":71434111222013,"sku":"9781804617755","price":77.54,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/3414\/0157\/files\/KCFQZp33Is9781804617755.webp?v=1790790736","url":"https:\/\/booktolia.com\/products\/deep-learning-and-xai-techniques-for-anomaly-detection-integrate-the-theory-and-practice-of-deep-anomaly-explainability-paperback","provider":"booktolia","version":"1.0","type":"link"}