Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-Based Libraries, Extensions, and Frameworks by Mishra, Pradeepta
Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-Based Libraries, Extensions, and Frameworks by Mishra, Pradeepta

Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-Based Libraries, Extensions, and Frameworks

Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make...
$135.88 AUD
$135.88 AUD
SKU: 9781484271575
Product Type: Books
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Author: Pradeepta Mishra
Format: Paperback
Language: English
Subtotal: $135.88
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Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-Based Libraries, Extensions, and Frameworks by Mishra, Pradeepta

Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-Based Libraries, Extensions, and Frameworks

$135.88
Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-Based Libraries, Extensions, and Frameworks by Mishra, Pradeepta

Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-Based Libraries, Extensions, and Frameworks

$135.88
Author: Pradeepta Mishra
Format: Paperback
Language: English
Description
Learn 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.



Author: Pradeepta Mishra
Publisher: Apress
Published: 12/15/2021
Pages: 344
Binding Type: Paperback
Weight: 1.39lbs
Size: 10.00h x 7.00w x 0.75d
ISBN: 9781484271575

This title is not returnable
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