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Deep Learning With PyTorch

2020; IGI Global; Linguagem: Inglês

10.4018/978-1-7998-3095-5.ch003

2327-0403

Anmol Chaudhary, Kuldeep Chouhan, Jyoti Gajrani, Bhavna Sharma,

Machine Learning and Data Classification

In the last decade, deep learning has seen exponential growth due to rise in computational power as a result of graphics processing units (GPUs) and a large amount of data due to the democratization of the internet and smartphones. This chapter aims to throw light on both the theoretical aspects of deep learning and its practical aspects using PyTorch. The chapter primarily discusses new technologies using deep learning and PyTorch in detail. The chapter discusses the advantages of using PyTorch compared to other deep learning libraries. The chapter discusses some of the practical applications like image classification and machine translation. The chapter also discusses the various frameworks built with the help of PyTorch. PyTorch consists of various models that increases its flexibility and accessibility to a greater extent. As a result, many frameworks built on top of PyTorch are discussed in this chapter. The authors believe that this chapter will help readers in getting a better understanding of deep learning making neural networks using PyTorch.