| Tag |
First Indicator |
Second Indicator |
Subfields |
| LEADER |
00000cgm a2200000 i 4500 |
| 001 |
in00005584813 |
| 006 |
m o c |
| 007 |
cr cna|||||||| |
| 007 |
vz czazuu |
| 008 |
181128s2018 xx 113 o vleng d |
| 005 |
20241201232930.2 |
| 035 |
|
|
|a (OCoLC)on1076490701
|
| 040 |
|
|
|a UMI
|b eng
|e rda
|e pn
|c UMI
|d TOH
|d OCLCF
|d ALSTP
|d OCLCO
|d OCL
|d OCLCA
|d OCLCQ
|d OCLCO
|d OCLCL
|
| 035 |
|
|
|a (OCoLC)1076490701
|
| 037 |
|
|
|a CL0501000008
|b Safari Books Online
|
| 050 |
|
4 |
|a QA76.87
|
| 049 |
|
|
|a TXAM
|
| 100 |
1 |
|
|a Sewak, Mohit,
|e author.
|
| 245 |
1 |
0 |
|a Practical convolutional neural networks /
|c Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari.
|
| 264 |
|
1 |
|a [Place of publication not identified] :
|b Packt,
|c [2018]
|
| 300 |
|
|
|a 1 online resource (1 streaming video file (1 hr., 52 min., 38 sec.))
|
| 336 |
|
|
|a two-dimensional moving image
|b tdi
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 337 |
|
|
|a video
|b v
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Title from title screen (viewed November 26, 2018).
|
| 500 |
|
|
|a Date of publication from resource description page.
|
| 520 |
|
|
|a "Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative e-commerce, and more. You will learn to create innovative solutions around image and video analytics to solve complex machine learning- and computer vision-related problems and implement real-life CNN models. This course starts with an overview of deep neural networks using image classification as an example and walks you through building your first CNN: a human face detector. You will learn to use concepts such as transfer learning with CNN and auto-encoders to build very powerful models, even when little-supervised training data for labeled images is available. Later we build upon this to build advanced vision-related algorithms for object detection, instance segmentation, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this course, you should be ready to implement advanced, effective, and efficient CNN models professionally or personally, by working on a complex image and video datasets."--Resource description page
|
| 650 |
|
0 |
|a Neural networks (Computer science)
|
| 650 |
|
0 |
|a Computer vision.
|
| 650 |
|
0 |
|a Machine learning.
|
| 650 |
|
0 |
|a Python (Computer program language)
|
| 650 |
|
2 |
|a Neural Networks, Computer
|
| 650 |
|
6 |
|a Réseaux neuronaux (Informatique)
|
| 650 |
|
6 |
|a Vision par ordinateur.
|
| 650 |
|
6 |
|a Apprentissage automatique.
|
| 650 |
|
6 |
|a Python (Langage de programmation)
|
| 650 |
|
7 |
|a Computer vision.
|2 fast
|0 (OCoLC)fst00872687
|
| 650 |
|
7 |
|a Machine learning.
|2 fast
|0 (OCoLC)fst01004795
|
| 650 |
|
7 |
|a Neural networks (Computer science)
|2 fast
|0 (OCoLC)fst01036260
|
| 650 |
|
7 |
|a Python (Computer program language)
|2 fast
|0 (OCoLC)fst01084736
|
| 655 |
|
7 |
|a Instructional films.
|2 fast
|0 (OCoLC)fst01726236
|
| 655 |
|
7 |
|a Instructional films.
|2 lcgft
|
| 655 |
|
7 |
|a Films de formation.
|2 rvmgf
|
| 700 |
1 |
|
|a Karim, Rezaul,
|e author.
|
| 700 |
1 |
|
|a Pujari, Pradeep,
|e author.
|
| 758 |
|
|
|i has work:
|a Practical convolutional neural networks (MovingImage)
|1 https://id.oclc.org/worldcat/entity/E39PCFv38qRdKGgPTGK6GmKFmm
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://www.aspresolver.com/aspresolver.asp?MARC;4072588
|z Connect to this streaming video (Alexander Street Press)
|t 0
|
| 955 |
|
|
|a Alexander Street Press subscription streaming video
|
| 994 |
|
|
|a 92
|b TXA
|
| 999 |
f |
f |
|s c0520483-5376-42dd-9f24-eed2aade8b76
|i 2d5a6e27-9504-41cb-99cb-efc2efa2776b
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|d Available Online
|t 0
|e QA76.87
|h Library of Congress classification
|
| 998 |
f |
f |
|a QA76.87
|t 0
|l Available Online
|