Abstract
The recent years have witnessed major releases of frameworks and tools to democratize deep learning to the masses. Today, we have a plethora of options at our disposal. A few popular names include PyTorch, TensorFlow, Keras, and MXNet—the list is never-ending. This chapter aims to provide an overview of PyTorch. We will be using PyTorch extensively throughout the book for implementing deep learning examples. Note that this chapter is not a comprehensive guide for PyTorch, so you should consult the additional materials suggested in the chapter for a deeper understanding of the framework. A basic overview will be offered and the necessary additions to the topic will be provided in the course of the examples implemented later in the book.
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© 2021 Nikhil Ketkar, Jojo Moolayil
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Ketkar, N., Moolayil, J. (2021). Introduction to PyTorch. In: Deep Learning with Python. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-5364-9_2
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DOI: https://doi.org/10.1007/978-1-4842-5364-9_2
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-5363-2
Online ISBN: 978-1-4842-5364-9
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