Titlebook: Deep Learning and Practice with MindSpore; Lei Chen Book 2021 Tsinghua University Press 2021 Deep Learning.MindSpore.Deep Neural Networks

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書目名稱Deep Learning and Practice with MindSpore網(wǎng)絡(luò)公開度學科排名




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書目名稱Deep Learning and Practice with MindSpore被引頻次學科排名




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書目名稱Deep Learning and Practice with MindSpore年度引用學科排名




書目名稱Deep Learning and Practice with MindSpore讀者反饋




書目名稱Deep Learning and Practice with MindSpore讀者反饋學科排名

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Deep Learning Visualization,explain why certain models excel on specific problems. Consequently, locating errors that occur in the models, and performing the subsequent code debugging, is a difficult process. Developers and model users alike therefore urgently need a method to help them explain, debug, and optimize deep learning models.

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Book 2021ral networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources..

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2520-1956 learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources..978-981-16-2235-9978-981-16-2233-5Series ISSN 2520-1956 Series E-ISSN 2520-1964

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2520-1956 ous applications and their performance in this regard.Combin.This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks

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Models of Hypertext Structure and Learningmaximally retaining the original semantics of the word. In this important tool for understanding natural language, we can use a word vector as the smallest unit for mining corpus data or as an input to complex models.

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Designing Impedance Networks Convertersneed to analyze and process graph data. One effective method for graph analysis is to map a graph’s elements to a low-dimensional vector space while retaining the graph’s structure and property information. This low-dimensional vector is called a graph vector (or “graph embedding”), which is described below.

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