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001443625 001__ 1443625
001443625 003__ OCoLC
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001443625 019__ $$a1290813774$$a1290840510$$a1294157487
001443625 020__ $$a9781484271582$$q(electronic bk.)
001443625 020__ $$a1484271580$$q(electronic bk.)
001443625 020__ $$z1484271572
001443625 020__ $$z9781484271575
001443625 0247_ $$a10.1007/978-1-4842-7158-2$$2doi
001443625 035__ $$aSP(OCoLC)1291318520
001443625 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dYDX$$dORMDA$$dGW5XE$$dSFB$$dOCLCO$$dOCLCF$$dOCLCQ
001443625 049__ $$aISEA
001443625 050_4 $$aQA76.73.P98$$b.M57 2021eb
001443625 08204 $$a006.3$$223
001443625 1001_ $$aMishra, Pradeepta.
001443625 24510 $$aPractical explainable AI using Python :$$bartificial intelligence model explanations using Python-based libraries, extensions, and frameworks /$$cPradeepta Mishra.
001443625 260__ $$aBerkeley, CA :$$bApress L.P.,$$c2022.
001443625 300__ $$a1 online resource (356 pages)
001443625 336__ $$atext$$btxt$$2rdacontent
001443625 337__ $$acomputer$$bc$$2rdamedia
001443625 338__ $$aonline resource$$bcr$$2rdacarrier
001443625 500__ $$aIncludes index.
001443625 5050_ $$aChapter 1: Introduction to Model Explainability and Interpretability -- Chapter 2: AI Ethics, Biasness and Reliability -- Chapter 3: Model Explainability for Linear Models Using XAI Components -- Chapter 4: Model Explainability for Non-Linear Models using XAI Components -- Chapter 5: Model Explainability for Ensemble Models Using XAI Components -- Chapter 6: Model Explainability for Time Series Models using XAI Components -- Chapter 7: Model Explainability for Natural Language Processing using XAI Components -- Chapter 8: AI Model Fairness Using What-If Scenario -- Chapter 9: Model Explainability for Deep Neural Network Models -- Chapter 10: Counterfactual Explanations for XAI models -- Chapter 11: Contrastive Explanation for Machine Learning -- Chapter 12: Model-Agnostic Explanations By Identifying Prediction Invariance -- Chapter 13: Model Explainability for Rule based Expert System -- Chapter 14: Model Explainability for Computer Vision.
001443625 506__ $$aAccess limited to authorized users.
001443625 520__ $$aLearn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data, classification problems, and natural language processing-related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks. What You'll Learn Review the different ways of making an AI model interpretable and explainable Examine the biasness and good ethical practices of AI models Quantify, visualize, and estimate reliability of AI models Design frameworks to unbox the black-box models Assess the fairness of AI models Understand the building blocks of trust in AI models Increase the level of AI adoption Who This Book Is For AI engineers, data scientists, and software developers involved in driving AI projects/ AI products.
001443625 588__ $$aDescription based on print version record.
001443625 650_0 $$aPython (Computer program language)
001443625 650_0 $$aArtificial intelligence$$xData processing.
001443625 650_0 $$aApplication software$$xDevelopment.
001443625 650_6 $$aPython (Langage de programmation)
001443625 650_6 $$aIntelligence artificielle$$xInformatique.
001443625 650_6 $$aLogiciels d'application$$xDéveloppement.
001443625 655_0 $$aElectronic books.
001443625 77608 $$iPrint version:$$aMishra, Pradeepta.$$tPractical Explainable AI Using Python.$$dBerkeley, CA : Apress L.P., ©2021$$z9781484271575
001443625 852__ $$bebk
001443625 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-7158-2$$zOnline Access$$91397441.1
001443625 909CO $$ooai:library.usi.edu:1443625$$pGLOBAL_SET
001443625 980__ $$aBIB
001443625 980__ $$aEBOOK
001443625 982__ $$aEbook
001443625 983__ $$aOnline
001443625 994__ $$a92$$bISE