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PyTorch recipesa problem-solution ap...

Mishra, Pradeepta.

PyTorch recipesa problem-solution approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	PyTorch recipesby Pradeepta Mishra.
Reminder of title:	a problem-solution approach /
Author:	Mishra, Pradeepta.
Published:	Berkeley, CA :Apress :2019.
Description:	xx, 184 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Neural networks (Computer science)
Online resource:	https://doi.org/10.1007/978-1-4842-4258-2
ISBN:	9781484242582$q(electronic bk.)

PyTorch recipesa problem-solution approach /
Mishra, Pradeepta.

PyTorch recipesa problem-solution approach /[electronic resource] :by Pradeepta Mishra. - Berkeley, CA :Apress :2019. - xx, 184 p. :ill., digital ;24 cm.

Chapter 1: Introduction PyTorch, Tensors, Tensor Operations and Basics -- Chapter 2: Probability distributions using PyTorch -- Chapter 3: Convolutional Neural Network and RNN using PyTorch -- Chapter 4: Introduction to Neural Networks, Tensor Differentiation -- Chapter 5: Supervised Learning using PyTorch -- Chapter 6: Fine Tuning Deep Learning Algorithms using PyTorch -- Chapter 7: NLP and Text Processing using PyTorch.

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.

ISBN: 9781484242582$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-4258-2doiSubjects--Topical Terms:

181982
Neural networks (Computer science)

LC Class. No.: QA76.87 / .M574 2019

Dewey Class. No.: 006.32

PyTorch recipesa problem-solution approach /
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505 0 $a Chapter 1: Introduction PyTorch, Tensors, Tensor Operations and Basics -- Chapter 2: Probability distributions using PyTorch -- Chapter 3: Convolutional Neural Network and RNN using PyTorch -- Chapter 4: Introduction to Neural Networks, Tensor Differentiation -- Chapter 5: Supervised Learning using PyTorch -- Chapter 6: Fine Tuning Deep Learning Algorithms using PyTorch -- Chapter 7: NLP and Text Processing using PyTorch.
520 $a 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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