國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

PyTorch recipesa problem-solution ap...

Mishra, Pradeepta.

PyTorch recipesa problem-solution approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	PyTorch recipesby Pradeepta Mishra.
其他題名:	a problem-solution approach /
作者:	Mishra, Pradeepta.
出版者:	Berkeley, CA :Apress :2019.
面頁冊數:	xx, 184 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Neural networks (Computer science)
電子資源:	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 /
LDR:02689nmm a2200325 a 4500 001 553010
003 DE-He213
005 20190813154831.0
006 m d
007 cr nn 008maaau
008 191107s2019 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 834026
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 181982
650 0 $a Machine learning. $3 188639
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Python. $3 763308
650 2 4 $a Big Data. $3 760530
650 2 4 $a Big Data/Analytics. $3 742047
710 2 $a SpringerLink (Online service) $3 273601
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)