{"product_id":"practical-explainable-ai-using-python-artificial-intelligence-model-explanations-using-python-based-libraries-extensions-and-frameworks-paperback","title":"Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-Based Libraries, Extensions, and Frameworks - 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\u003ePradeepta Mishra\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003eLearn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers.\u003cbr\u003eYou'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eFurther, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data, classification problems, and natural language processing-related tasks. Additionally, the book looks at counterfactual explanations for AI models. \u003ci\u003ePractical Explainable AI Using Python\u003c\/i\u003e shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks.\u003cbr\u003e\u003cb\u003eWhat You'll Learn\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eReview the different ways of making an AI model interpretable and explainable\u003c\/li\u003e\n\u003cli\u003eExamine the biasness and good ethical practices of AI models\u003c\/li\u003e\n\u003cli\u003eQuantify, visualize, and estimate reliability of AI models\u003c\/li\u003e\n\u003cli\u003eDesign frameworks to unbox the black-box models\u003c\/li\u003e\n\u003cli\u003eAssess the fairness of AI models\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eUnderstand the building blocks of trust in AI models\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eIncrease the level of AI adoption\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003e\u003cbr\u003e\u003c\/b\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003cbr\u003eAI engineers, data scientists, and software developers involved in driving AI projects\/ AI products. \u003cp\u003e\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003eLearn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers.\u003cbr\u003eYou'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eFurther, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data and natural language processing-related tasks. Additionally, the book looks at counterfactual explanations for AI models. \u003ci\u003ePractical Explainable AI Using Python\u003c\/i\u003e shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks.\u003cbr\u003eYou will: \u003cul\u003e\n\u003cli\u003eReview the different ways of making an AI model interpretable and explainable\u003c\/li\u003e\n\u003cli\u003eExamine the biasness and good ethical practices of AI models\u003c\/li\u003e\n\u003cli\u003eQuantify, visualize, and estimate reliability of AI models\u003c\/li\u003e\n\u003cli\u003eDesign frameworks to unbox the black-box models\u003c\/li\u003e\n\u003cli\u003eAssess the fairness of AI models\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eUnderstand the building blocks of trust in AI models\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eIncrease the level of AI adoption\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003ePradeepta Mishra is the Head of AI (Leni) at L\u0026amp;T Infotech (LTI), leading a large group of data scientists, computational linguistics experts, machine learning and deep learning experts in building next generation product, 'Leni' world's first virtual data scientist. He was awarded as \"India's Top - 40Under40DataScientists\" by Analytics India Magazine. He is an author of 4 books, his first book has been recommended in HSLS center at the University of Pittsburgh, PA, USA. His latest book #PytorchRecipes was published by Apress. He has delivered a keynote session at the Global Data Science conference 2018, USA. He has delivered a TEDx talk on \"Can Machines Think?\", available on the official TEDx YouTube channel. He has delivered 200+ tech talks on data science, ML, DL, NLP, and AI in various Universities, meetups, technical institutions and community arranged forums.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 344\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.75 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e December 15, 2021\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47213286949113,"sku":"9781484271575","price":75.58,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/dHVORFNnQlJ2V0xvTlMwZW5rRWFxQT09.webp?v=1768104490","url":"https:\/\/bookscloud.io\/products\/practical-explainable-ai-using-python-artificial-intelligence-model-explanations-using-python-based-libraries-extensions-and-frameworks-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}