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PyTorch recipes : a problem-solution approach

Author: Pradeepta Mishra
Publisher: [California] : Apress, [2019] ©2019
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using PyTorch and get acquainted with its concepts. Further you will dive into  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
(OCoLC)1056741289
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Pradeepta Mishra
ISBN: 9781484242582 1484242580 9781484242599 1484242599
OCLC Number: 1083641773
Notes: Includes index.
Description: 1 online resource
Contents: Introduction to PyTorch, Tensors, and Tensor operations --
Probability distributions using PyTorch --
CNN and RNN using PyTorch --
Introduction to neural networks using PyTorch --
Supervised learning using PyTorch --
Fine-tuning deep learning models using PyTorch --
Natural language processing using PyTorch.
Responsibility: Pradeepta Mishra.

Abstract:

Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using PyTorch and get acquainted with its concepts. Further you will dive into transformations and graph computations with PyTorch. Along the way you will take a look at common issues faced with neural network implementation and tensor differentiation, and get the best solutions for them. Moving on to algorithms; you will learn how PyTorch works with supervised and unsupervised algorithms. You will see how convolutional neural networks, deep neural networks, and recurrent neural networks work using PyTorch. In conclusion you will get acquainted with natural language processing and text processing using PyTorch. You will: Master tensor operations for dynamic graph-based calculations using PyTorch Create PyTorch transformations and graph computations for neural networks Carry out supervised and unsupervised learning using PyTorch Work with deep learning algorithms such as CNN and RNN Build LSTM models in PyTorch Use PyTorch for text processing.

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