東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

PyTorch recipes = a problem-solution...

Mishra, Pradeepta.

Linked to FindBook

Google Book

Amazon

博客來

PyTorch recipes = a problem-solution approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	PyTorch recipes/ by 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.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/978-1-4842-4258-2
ISBN:	9781484242582

PyTorch recipes = a 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

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

532070
Neural networks (Computer science)

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

Dewey Class. No.: 006.32

PyTorch recipes = a problem-solution approach /
LDR:02689nmm a2200325 a 4500 001 2179618
003 DE-He213
005 20190813154831.0
006 m d
007 cr nn 008maaau
008 191122s2019 cau s 0 eng d
020 $a 9781484242582 $q (electronic bk.)
020 $a 9781484242575 $q (paper)
024 7 $a 10.1007/978-1-4842-4258-2 $2 doi
035 $a 978-1-4842-4258-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87 $b .M574 2019
072 7 $a UMX $2 bicssc
072 7 $a COM051360 $2 bisacsh
072 7 $a UMX $2 thema
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .M678 2019
100 1 $a Mishra, Pradeepta. $3 3384947
245 1 0 $a PyTorch recipes $h [electronic resource] : $b a problem-solution approach / $c by Pradeepta Mishra.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a xx, 184 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Neural networks (Computer science) $3 532070
650 0 $a Machine learning. $3 533906
650 0 $a Python (Computer program language) $3 729789
650 1 4 $a Python. $3 3201289
650 2 4 $a Big Data. $3 3134868
650 2 4 $a Big Data/Analytics. $3 2186785
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-4258-2
950 $a Professional and Applied Computing (Springer-12059)