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Paperback or Softback. Condition: New. Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python. Book.
Seller: Lakeside Books, Benton Harbor, MI, U.S.A.
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Condition: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
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Paperback. Condition: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.
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Seller: BargainBookStores, Grand Rapids, MI, U.S.A.
Paperback or Softback. Condition: New. Pytorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models. Book.
Seller: Lakeside Books, Benton Harbor, MI, U.S.A.
Condition: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!
Paperback. Condition: New. Second Edition. Learn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new chapters covering topics such as distributed PyTorch modeling, deploying PyTorch models in production, and developments around PyTorch with updated code.You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as LSTMs, and RNNs. Next, you'll explore probability distribution concepts using PyTorch, as well as supervised and unsupervised algorithms with PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural networks using PyTorch. This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library, model quantization to reduce parameter size, and preparing a model for deployment within a production system. Distributed parallel processing for balancing PyTorch workloads, using PyTorch for image processing, audio analysis, and model interpretation are also covered in detail. Each chapter includes recipe code snippets to perform specific activities.By the end of this book, you will be able to confidently build neural network models using PyTorch.What You Will LearnUtilize new code snippets and models to train machine learning models using PyTorchTrain deep learning models with fewer and smarter implementationsExplore the PyTorch framework for model explainability and to bring transparency to model interpretationBuild, train, and deploy neural network models designed to scale with PyTorchUnderstand best practices for evaluating and fine-tuning models using PyTorchUse advanced torch features in training deep neural networksExplore various neural network models using PyTorchDiscover functions compatible with sci-kit learn compatible modelsPerform distributed PyTorch training and executionWho This Book Is ForMachine learning engineers, data scientists and Python programmers and software developers interested in learning the PyTorch framework.
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Seller: California Books, Miami, FL, U.S.A.
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Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
First Edition
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Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Add to basketPaperback. Condition: Brand New. 278 pages. 9.25x6.10x0.59 inches. In Stock.
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Condition: New. 2023. 1st ed. paperback. . . . . . Books ship from the US and Ireland.
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Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
First Edition
Paperback. Condition: new. Paperback. 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 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 unstructured data, classification problems, and natural language processingrelated 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 LearnReview the different ways of making an AI model interpretable and explainableExamine the biasness and good ethical practices of AI modelsQuantify, visualize, and estimate reliability of AI modelsDesign frameworks to unbox the black-box modelsAssess the fairness of AI modelsUnderstand the building blocks of trust in AI modelsIncrease the level of AI adoptionWho This Book Is ForAI engineers, data scientists, and software developers involved in driving AI projects/ AI products. Intermediate-Advanced Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Seller: Chiron Media, Wallingford, United Kingdom
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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