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Publicado por Packt Publishing - ebooks Account, 2021
ISBN 10: 1789614384 ISBN 13: 9781789614381
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Añadir al carritoPaperback. Condición: Used; Very Good. ***Simply Brit*** Welcome to our online used book store, where affordability meets great quality. Dive into a world of captivating reads without breaking the bank. We take pride in offering a wide selection of used books, from classics to hidden gems, ensuring there is something for every literary palate. All orders are shipped within 24 hours and our lightning fast-delivery within 48 hours coupled with our prompt customer service ensures a smooth journey from ordering to delivery. Discover the joy of reading with us, your trusted source for affordable books that do not compromise on quality.
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ISBN 10: 1789614384 ISBN 13: 9781789614381
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Añadir al carritoPaperback or Softback. Condición: New. Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features. Book.
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Publicado por Packt Publishing 2021-02-12, 2021
ISBN 10: 1789614384 ISBN 13: 9781789614381
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Publicado por Packt Publishing Limited, 2021
ISBN 10: 1789614384 ISBN 13: 9781789614381
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Discover the flexibility of the PyTorch library for implementing new algorithms in a scalable and efficient way with this expert guide. This book will show you how to process data with deep learning methodologies using PyTorch 1.x and cover advanced topics .
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Añadir al carritoTaschenbuch. Condición: Neu. Mastering PyTorch | Build powerful neural network architectures using advanced PyTorch 1.x features | Ashish Ranjan Jha | Taschenbuch | Kartoniert / Broschiert | Englisch | 2021 | Packt Publishing | EAN 9781789614381 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Master advanced techniques and algorithms for deep learning with PyTorch using real-world examplesKey Features Understand how to use PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much moreBook DescriptionDeep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models.The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai.By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text and music generating models using PyTorch Build a deep Q-network (DQN) model in PyTorch Export universal PyTorch models using Open Neural Network Exchange (ONNX) Become well-versed with rapid prototyping using PyTorch with fast.ai Perform neural architecture search effectively using AutoML Easily interpret machine learning (ML) models written in PyTorch using Captum Design ResNets, LSTMs, Transformers, and more using PyTorch Find out how to use PyTorch for distributed training using the torch.distributed APIWho this book is forThis book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.Table of Contents Overview of Deep Learning Using PyTorch Combining CNNs and LSTMs Deep CNN Architectures Deep Recurrent Model Architectures Hybrid Advanced Models Music and Text Generation with PyTorch Neural Style Transfer Deep Convolutional GANs Deep Reinforcement Learning Operationalizing Pytorch Models into Production Distributed Training PyTorch and AutoML PyTorch and Explainable AI Rapid Prototyping with PyTorch.