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PyTorch Deep Learning Hands-On

You're reading from   PyTorch Deep Learning Hands-On Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788834131
Length 250 pages
Edition 1st Edition
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Authors (2):
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Sherin Thomas Sherin Thomas
Author Profile Icon Sherin Thomas
Sherin Thomas
Sudhanshu Passi Sudhanshu Passi
Author Profile Icon Sudhanshu Passi
Sudhanshu Passi
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Chapter 3. Deep Learning Workflow

Although deep learning is making a big shift from just being in academia to being in industry and powering millions of users' requests every day, the new players in the field still struggle to set up a workflow for the deep learning pipeline. This chapter is designed to cover the portion of workflow that PyTorch can help with.

PyTorch was started as a research framework by a Facebook intern and it has grown to the stage where the backend is backed by a super-optimized Caffe2 core. So, in a nutshell, PyTorch can be used as a research or prototype framework and at the same time, it can be used to write an efficient model with serving modules, and it also can be deployable to single-board computers and mobile devices.

A typical deep learning workflow starts with ideation and research around a problem statement, which is where the architectural design and model decisions come into play. The theoretical model is then experimented...

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