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001454707 001__ 1454707
001454707 003__ OCoLC
001454707 005__ 20230314003224.0
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001454707 007__ cr\cn\nnnunnun
001454707 008__ 230220s2023\\\\xx\\\\\\o\\\\\001\0\eng\d
001454707 019__ $$a1370391737$$a1370494063
001454707 020__ $$a9781484290293$$q(electronic bk.)
001454707 020__ $$a1484290291$$q(electronic bk.)
001454707 020__ $$z1484290283
001454707 020__ $$z9781484290286
001454707 0247_ $$a10.1007/978-1-4842-9029-3$$2doi
001454707 035__ $$aSP(OCoLC)1370607273
001454707 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dORMDA
001454707 049__ $$aISEA
001454707 050_4 $$aQ335
001454707 08204 $$a006.3$$223/eng/20230220
001454707 1001_ $$aMishra, Pradeepta,$$eauthor.
001454707 24510 $$aExplainable AI recipes :$$bimplement solutions to model explainability and interpretability with Python /$$cPradeepta Mishra.
001454707 264_1 $$a[Place of publication not identified] :$$bApress,$$c2023.
001454707 300__ $$a1 online resource (253 pages) :$$billustrations (black and white, and colour).
001454707 336__ $$atext$$btxt$$2rdacontent
001454707 337__ $$acomputer$$bc$$2rdamedia
001454707 338__ $$aonline resource$$bcr$$2rdacarrier
001454707 500__ $$aIncludes index.
001454707 5050_ $$aChapter 1: Introduction to Explainability Library Installations -- Chapter 2: Linear Supervised Model Explainability -- Chapter 3: Non-Linear Supervised Learning Model Explainability -- Chapter 4: Ensemble Model for Supervised Learning Explainability -- Chapter 5: Explainability for Natural Language Modeling -- Chapter 6: Time Series Model Explainability -- Chapter 7: Deep Neural Network Model Explainability.
001454707 506__ $$aAccess limited to authorized users.
001454707 520__ $$aUnderstand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. You will: Create code snippets and explain machine learning models using Python Leverage deep learning models using the latest code with agile implementations Build, train, and explain neural network models designed to scale Understand the different variants of neural network models.
001454707 588__ $$aDescription based on print version record.
001454707 650_0 $$aArtificial intelligence.
001454707 650_0 $$aPython (Computer program language)
001454707 655_0 $$aElectronic books.
001454707 77608 $$iPrint version:$$aMISHRA, PRADEEPTA.$$tEXPLAINABLE AI RECIPES.$$d[Place of publication not identified] : APRESS, 2023$$z1484290283$$w(OCoLC)1346535007
001454707 852__ $$bebk
001454707 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-9029-3$$zOnline Access$$91397441.1
001454707 909CO $$ooai:library.usi.edu:1454707$$pGLOBAL_SET
001454707 980__ $$aBIB
001454707 980__ $$aEBOOK
001454707 982__ $$aEbook
001454707 983__ $$aOnline
001454707 994__ $$a92$$bISE