|
|
|
|
LEADER |
00000nam a22000005i 4500 |
001 |
b3253902 |
003 |
MWH |
005 |
20190704141649.0 |
007 |
cr nn 008mamaa |
008 |
190128s2019 xxu| s |||| 0|eng d |
020 |
|
|
|a 9781484242582
|
024 |
7 |
|
|a 10.1007/978-1-4842-4258-2
|2 doi
|
035 |
|
|
|a (DE-He213)978-1-4842-4258-2
|
050 |
|
4 |
|a E-Book
|
072 |
|
7 |
|a UMX
|2 bicssc
|
072 |
|
7 |
|a COM051360
|2 bisacsh
|
072 |
|
7 |
|a UMX
|2 thema
|
100 |
1 |
|
|a Mishra, Pradeepta.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
|
245 |
1 |
0 |
|a PyTorch Recipes
|h [electronic resource] :
|b A Problem-Solution Approach /
|c by Pradeepta Mishra.
|
250 |
|
|
|a 1st ed. 2019.
|
264 |
|
1 |
|a Berkeley, CA :
|b Apress :
|b Imprint: Apress,
|c 2019.
|
300 |
|
|
|a XX, 184 p. 280 illus.
|b online resource.
|
336 |
|
|
|a text
|b txt
|2 rdacontent
|
337 |
|
|
|a computer
|b c
|2 rdamedia
|
338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
347 |
|
|
|a text file
|b PDF
|2 rda
|
490 |
1 |
|
|a Springer eBook Collection
|
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.
|
590 |
|
|
|a Loaded electronically.
|
590 |
|
|
|a Electronic access restricted to members of the Holy Cross Community.
|
650 |
|
0 |
|a Python (Computer program language).
|
650 |
|
0 |
|a Big data.
|
690 |
|
|
|a Electronic resources (E-books)
|
710 |
2 |
|
|a SpringerLink (Online service)
|
773 |
0 |
|
|t Springer eBooks
|
830 |
|
0 |
|a Springer eBook Collection.
|
856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://doi.org/10.1007/978-1-4842-4258-2
|3 Click to view e-book
|t 0
|
907 |
|
|
|a .b32539022
|b 04-18-22
|c 02-26-20
|
998 |
|
|
|a he
|b 02-26-20
|c m
|d @
|e -
|f eng
|g xxu
|h 0
|i 1
|
912 |
|
|
|a ZDB-2-CWD
|
950 |
|
|
|a Professional and Applied Computing (Springer-12059)
|
902 |
|
|
|a springer purchased ebooks
|
903 |
|
|
|a SEB-COLL
|
945 |
|
|
|f - -
|g 1
|h 0
|j - -
|k - -
|l he
|o -
|p $0.00
|q -
|r -
|s b
|t 38
|u 0
|v 0
|w 0
|x 0
|y .i2167064x
|z 02-26-20
|
999 |
f |
f |
|i d9fea8f7-b377-5697-bcb1-401aadc13d01
|s e89a98cc-11b9-54cd-83c8-41a1d4bff5db
|t 0
|
952 |
f |
f |
|p Online
|a College of the Holy Cross
|b Main Campus
|c E-Resources
|d Online
|t 0
|e E-Book
|h Library of Congress classification
|i Elec File
|