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Paperback. Zustand: 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.…

Zustand: 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 or Softback. Zustand: New. Pytorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models. Book.

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Zustand: New. 2023. 1st ed. paperback. . . . . .

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Paperback or Softback. Zustand: New. Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks. Book.

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Paperback. Zustand: Brand New. 278 pages. 9.25x6.10x0.59 inches. In Stock.

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Paperback / softback. Zustand: New. New copy - Usually dispatched within 2 working days.

Zustand: New. 2023. 1st ed. paperback. . . . . . Books ship from the US and Ireland.

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paperback. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 3-4 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Zustand: New. 2022. 2nd Edition. paperback. . . . . .

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