{"product_id":"practical-explainable-ai-using-python-9781484271575","title":"Practical Explainable AI Using Python","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\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.   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 decisionFurther, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, youwill be introduced to model explainability for unstructur\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“Practical explainable AI using Python combines textbook and cookbook elements. It provides explanations of concepts along with practical examples and exercises. … this book offers a comprehensive foundation that will remain relevant for some time. However, readers should supplement their knowledge with the latest research in order to stay up to date in this dynamic field.” (Gulustan Dogan, Computing Reviews, August 21, 2023)\u003cbr\u003e\u003cbr\u003e“While the book presents just fundamental aspects, I find this to be a great advantage. Indeed, even the layperson to AI\/ML can use this work: the author starts with the most basic definitions and models, and then provides software examples … . This way a very broad readership is possible, since more advanced parts of the chapters will be interesting even for specialists in AI\/ML who would like to increase their expertise in the title topic.” (Piotr Cholda, Computing Reviews, April 17, 2023)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1:  Introduction to Model Explainability and InterpretabilityChapter Goal: This chapter is to understand what is model explainability and interpretability using Python. No of pages: 30-40 pages\t\u003cbr\u003eChapter 2:  AI Ethics, Biasness and Reliability Chapter Goal: This chapter aims at covering different frameworks using XAI Python libraries to control biasness, execute the principles of reliability and maintain ethics while generating predictions.No of pages: 30-40\u003cbr\u003eChapter 3: Model Explainability for Linear Models Using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by linear models for supervised learning task, for structured dataNo of pages : 30-40\u003cbr\u003eChapter 4: Model Explainability for Non-Linear Models using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by non-linear models, such as tree based models for supervised learning task, for structured dataNo of pages: 30-40\u003cbr\u003eChapter 5: Model Explainability for Ensemble Models Using XAI Components\u003cbr\u003eChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by ensemble models, such as tree based ensemble models for supervised learning task, for structured data No of pages: 30-40\u003cbr\u003eChapter 6: Model Explainability for Time Series Models using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by time series models for structured data, both univariate time series model and multivariate time series modelNo of pages: 30-40\u003cbr\u003eChapter 7: Model Explainability for Natural Language Processing using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by models from text classification, summarization, sentiment classification No of pages: 30-40\u003cbr\u003eChapter 8: AI Model Fairness Using What-If ScenarioChapter Goal: This chapter explains use of Google’s WIT Tool and custom libraries to explain the fairness of an AI modelNo of pages: 30-40\u003cbr\u003eChapter 9: Model Explainability for Deep Neural Network ModelsChapter Goal: This chapter explains use of Python libraries to interpret the neural network models and deep learning models such as LSTM models, CNN models etc. using smooth grad and deep shiftNo of pages: 30-40\u003cbr\u003eChapter 10: Counterfactual Explanations for XAI modelsChapter Goal: This chapter aims at providing counterfactual explanations to explain predictions of individual instances. The \"event\" is the predicted outcome of an instance, the \"cause\" are the particular feature values of this instance that were the input to the model that \"caused\" a certain prediction.No of pages: 30-40\u003cbr\u003eChapter 11: Contrastive Explanation for Machine Learning\u003cbr\u003eChapter Goal: In this chapter we will use foil trees: a model-agnostic approach to extracting explanations for finding the set of rules that causes the explanation to be predicted the actual outcome (fact) instead of the other (foil)No of pages: 20-30\u003cbr\u003eChapter 12: Model-Agnostic Explanations By Identifying Prediction InvarianceChapter Goal: In this chapter we will use anchor-LIME (a-LIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear.No of pages: 20-30\u003cbr\u003eChapter 13: Model Explainability for Rule based Expert System Chapter Goal: In this chapter we will use anchor-LIME (a-LIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear.No of pages: 20-30\u003cbr\u003eChapter 14: Model Explainability for Computer Vision.Chapter Goal: In this chapter we will use Python libraries to explain computer vision tasks such as object detection, image classification models.No of pages: 20-30\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":50051546775895,"sku":"9781484271575","price":46.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484271575.jpg?v=1740363118","url":"https:\/\/bookcurl.com\/products\/practical-explainable-ai-using-python-9781484271575","provider":"Book Curl","version":"1.0","type":"link"}