{"product_id":"interpretable-ai-pbuilding-explainable-machine-learning-systems-p","title":"Interpretable AI","description":"\u003cp\u003e\u003cspan class=\"a-text-bold\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-weight:\u003eAI 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.\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eIn \u003c\/strong\u003e\u003c\/span\u003e\u003cstrong\u003e\u003cspan class=\"a-text-italic\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-style: italic\u003eInterpretable AI\u003c\/span\u003e\u003c\/strong\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cstrong\u003e, you will learn:\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eWhy AI models are hard to interpret\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eInterpreting white box models such as linear regression, decision trees, and generalized additive models\u003cbr style=\"box-sizing: border-box;\"\u003ePartial dependence plots, LIME, SHAP and Anchors, and other techniques such as saliency mapping, network dissection, and representational learning\u003cbr style=\"box-sizing: border-box;\"\u003eWhat fairness is and how to mitigate bias in AI systems\u003cbr style=\"box-sizing: border-box;\"\u003eImplement robust AI systems that are GDPR-compliant\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cspan class=\"a-text-italic\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-style: italic\u003eInterpretable AI\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e 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.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003ePurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eAbout the technology\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eIt’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.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eAbout the book\u003c\/strong\u003e\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cspan class=\"a-text-italic\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-style: italic\u003eInterpretable AI\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e 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.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eWhat's inside\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e    Techniques for interpreting AI models\u003cbr style=\"box-sizing: border-box;\"\u003e    Counteract errors from bias, data leakage, and concept drift\u003cbr style=\"box-sizing: border-box;\"\u003e    Measuring fairness and mitigating bias\u003cbr style=\"box-sizing: border-box;\"\u003e    Building GDPR-compliant AI systems\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eAbout the reader\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eFor data scientists and engineers familiar with Python and machine learning.\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eAbout the author\u003c\/strong\u003e\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cspan class=\"a-text-bold\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-weight:\u003eAjay Thampi\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e is a machine learning engineer focused on responsible AI and fairness.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003ePART 1 INTERPRETABILITY BASICS\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e1 Introduction\u003cbr style=\"box-sizing: border-box;\"\u003e2 White-box models\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003ePART 2 INTERPRETING MODEL PROCESSING\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e3 Model-agnostic methods: Global interpretability\u003cbr style=\"box-sizing: border-box;\"\u003e4 Model-agnostic methods: Local interpretability\u003cbr style=\"box-sizing: border-box;\"\u003e5 Saliency mapping\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003ePART 3 INTERPRETING MODEL REPRESENTATIONS\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e6 Understanding layers and units\u003cbr style=\"box-sizing: border-box;\"\u003e7 Understanding semantic similarity\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003ePART 4 FAIRNESS AND BIAS\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e8 Fairness and mitigating bias\u003cbr style=\"box-sizing: border-box;\"\u003e9 Path to explainable AI\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEbook License\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eEnd-User Warranty And License Agreement\u003c\/p\u003e\u003cp\u003e1. Grant Of License\u003c\/p\u003e\u003cp\u003eManning Has Authorized The Download By You Of An Unrestricted Number Of Copies Of The Electronic Book (Ebook) In Any Of The Available Formats. Manning Grants You A Nonexclusive, Nontransferable License To Use The Ebook According To The Terms And Conditions Herein. This License Agreement Permits You To Install The Ebook On Any And All Your Devices For Your Personal Use Only.\u003c\/p\u003e\u003cp\u003e2. Restrictions\u003c\/p\u003e\u003cp\u003eYou Shall Not: (1) Share, Resell, Rent, Assign, Timeshare, Distribute, Or Transfer All Or Part Of The Ebook Or Any Rights Granted Hereunder To Any Other Person; (2) Duplicate The Ebook, Except For A Single Backup Or Archival Copy; (3) Remove Any Proprietary Notices, Labels, Or Marks From The Ebook; (4) Transfer Or Sublicense Title To The Ebook To Any Other Party.\u003c\/p\u003e\u003cp\u003e3. 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Limitation Of Liability\u003c\/p\u003e\u003cp\u003eIn No Event Will Manning Be Liable For Any Damages, Whether Arising For Tort Or Contract, Including Loss Of Data, Lost Profits, Or Other Special, Incidental, Consequential, Or Indirect Damages Arising Out Of The Use Or Inability To Use The Ebook.\u003c\/p\u003e\u003cp\u003e7. General\u003c\/p\u003e\u003cp\u003eThis Agreement Constitutes The Entire Agreement Between You And Manning And Supersedes Any Prior Agreement Concerning The Ebook. This Agreement Is Governed By The Laws Of The State Of New York. \u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Paperback","offer_id":50506263593030,"sku":"9781617297649","price":5318.0,"currency_code":"INR","in_stock":true},{"title":"E-Book","offer_id":50506263625798,"sku":"9781617297649-2","price":4333.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0754\/2797\/1142\/files\/Thampi-AI-HI.png?v=1790324565","url":"https:\/\/cb-india.com\/products\/interpretable-ai-pbuilding-explainable-machine-learning-systems-p","provider":"Computer Bookshop (India) Pvt Ltd","version":"1.0","type":"link"}