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Deep Learning with PyTorch 1.x: Implement deep learning techniques and neural network architecture variants using Python, 2nd Edition. Code by Laura Mitchell; Sri. Yogesh K.; Vishnu Subramanian is a nonfiction available to read on EtoBox.

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Code . Build and train neural network models with high speed and flexibility in text, vision, and advanced analytics using PyTorch 1.x Key Features Gain a thorough understanding of the PyTorch framework and learn to implement neural network architectures Understand GPU computing to perform heavy deep learning computations using Python Apply cutting-edge natural language processing (NLP) techniques to solve problems with textual data Book Description PyTorch is gaining the attention of deep learning researchers and data science professionals due to its accessibility and efficiency, along with the fact that it's more native to the Python way of development. This book will get you up and running with this cutting-edge deep learning library, effectively guiding you through implementing deep learning concepts. In this second edition, you'll learn the fundamental aspects that power modern deep learning, and explore the new features of the PyTorch 1.x library. You'll understand how to solve real-world problems using CNNs, RNNs, and LSTMs, along with discovering state-of-the-art modern deep learning architectures, such as ResNet, DenseNet, and Inception. You'll then focus on

Who reads Deep Learning with PyTorch 1.x: Implement deep learning techniques and neural network architecture variants using Python, 2nd Edition. Code?

Author

Laura Mitchell; Sri. Yogesh K.; Vishnu Subramanian

Publisher

Packt Publishing, Limited

Published

2019

Language

EN

ISBN

9781838550332

Category

nonfiction

Subjects

Science, Computer Science, Stem