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184017833
Interpretable AI: Building explainable machine learning systems
Item #:
184017833
MYR 371
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- AI doesn’t have to be a black box. These practical techniques help shine a light on your model’s mysterious inner workings. Make your AI more transparent, and you’ll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements.In Interpretable AI, you will learn:Why AI models are hard to interpretInterpreting white box models such as linear regression, decision trees, and generalized additive modelsPartial dependence plots, LIME, SHAP and Anchors, and other techniques such as saliency mapping, network dissection, and representational learningWhat fairness is and how to mitigate bias in AI systemsImplement robust AI systems that are GDPR-compliantInterpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You’ll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model.About the technologyIt’s often difficult to explain how deep learning models work, even for the data scientists who create them. Improving transparency and interpretability in machine learning models minimizes errors, reduces unintended bias, and increases trust in the outcomes. This unique book contains techniques for looking inside “black box” models, designing accountable algorithms, and understanding the factors that cause skewed results.About the bookInterpretable AI teaches you to identify the patterns your model has learned and why it produces its results. As you read, you’ll pick up algorithm-specific approaches, like interpreting regression and generalized additive models, along with tips to improve performance during training. You’ll also explore methods for interpreting complex deep learning models where some processes are not easily observable. AI transparency is a fast-moving field, and this book simplifies cutting-edge research into practical methods you can implement with Python.What's insideTechniques for interpreting AI modelsCounteract errors from bias, data leakage, and concept driftMeasuring fairness and mitigating biasBuilding GDPR-compliant AI systemsAbout the readerFor data scientists and engineers familiar with Python and machine learning.About the authorAjay Thampi is a machine learning engineer focused on responsible AI and fairness.Table of ContentsPART 1 INTERPRETABILITY BASICS1 Introduction2 White-box modelsPART 2 INTERPRETING MODEL PROCESSING3 Model-agnostic methods: Global interpretability4 Model-agnostic methods: Local interpretability5 Saliency mappingPART 3 INTERPRETING MODEL REPRESENTATIONS6 Understanding layers and units7 Understanding semantic similarityPART 4 FAIRNESS AND BIAS8 Fairness and mitigating bias9 Path to explainable AI
| Publisher | Manning |
| Publication date | July 5, 2022 |
| Language | English |
| Print length | 328 pages |
| ISBN-10 | 161729764X |
| ISBN-13 | 978-1617297649 |
| Item Weight | 1.34 pounds (610 grams) |
| Dimensions | 7.38 x 0.8 x 9.25 inches (18.7 x 2 x 23.5 cm) |
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MYR 371
Pesan sekarang dan dapatkannya lebih kurang pada Khamis, Oktober 15
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