Language:
English
繁體中文
Help
圖資館首頁
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Deep learning projects using TensorF...
~
Silaparasetty, Vinita.
Deep learning projects using TensorFlow 2neural network development with Python and Keras /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Deep learning projects using TensorFlow 2by Vinita Silaparasetty.
Reminder of title:
neural network development with Python and Keras /
Author:
Silaparasetty, Vinita.
Published:
Berkeley, CA :Apress :2020.
Description:
xxv, 421 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
Subject:
Machine learning.
Online resource:
https://doi.org/10.1007/978-1-4842-5802-6
ISBN:
9781484258026$q(electronic bk.)
Deep learning projects using TensorFlow 2neural network development with Python and Keras /
Silaparasetty, Vinita.
Deep learning projects using TensorFlow 2
neural network development with Python and Keras /[electronic resource] :by Vinita Silaparasetty. - Berkeley, CA :Apress :2020. - xxv, 421 p. :ill., digital ;24 cm.
Chapter 1: Getting Started: Installation and Troubleshooting -- Chapter 2: Perceptrons -- Chapter 3: Neural Networks -- Chapter 4: Sentiment Analysist -- Chapter 5: Music Generation -- Chapter 6: Image Colorization -- Chapter 7: Image Deblurring -- Chapter 8: Image Manipulation -- Chapter 9: Neutral Network Collection -- Appendix: Portfolio Tips.
Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to deep fakes and so much more. It is crucial for aspiring data scientists or those who want to enter the field of AI to understand deep learning concepts. The best way to learn is by doing. You'll develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. You'll also work with Neural Networks and other deep learning concepts. By the end of the book, you'll have a collection of unique projects that you can add to your GitHub profiles and expand on for professional application. You will: Grasp the basic process of neural networks through projects, such as creating music Restore and colorize black and white images with deep learning processes.
ISBN: 9781484258026$q(electronic bk.)
Standard No.: 10.1007/978-1-4842-5802-6doiSubjects--Uniform Titles:
TensorFlow.
Subjects--Topical Terms:
188639
Machine learning.
LC Class. No.: Q325.5
Dewey Class. No.: 006.31
Deep learning projects using TensorFlow 2neural network development with Python and Keras /
LDR
:02437nmm a2200325 a 4500
001
583933
003
DE-He213
005
20201130091651.0
006
m d
007
cr nn 008maaau
008
210202s2020 cau s 0 eng d
020
$a
9781484258026$q(electronic bk.)
020
$a
9781484258019$q(paper)
024
7
$a
10.1007/978-1-4842-5802-6
$2
doi
035
$a
978-1-4842-5802-6
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
Q325.5
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
006.31
$2
23
090
$a
Q325.5
$b
.S581 2020
100
1
$a
Silaparasetty, Vinita.
$3
874750
245
1 0
$a
Deep learning projects using TensorFlow 2
$h
[electronic resource] :
$b
neural network development with Python and Keras /
$c
by Vinita Silaparasetty.
260
$a
Berkeley, CA :
$b
Apress :
$b
Imprint: Apress,
$c
2020.
300
$a
xxv, 421 p. :
$b
ill., digital ;
$c
24 cm.
505
0
$a
Chapter 1: Getting Started: Installation and Troubleshooting -- Chapter 2: Perceptrons -- Chapter 3: Neural Networks -- Chapter 4: Sentiment Analysist -- Chapter 5: Music Generation -- Chapter 6: Image Colorization -- Chapter 7: Image Deblurring -- Chapter 8: Image Manipulation -- Chapter 9: Neutral Network Collection -- Appendix: Portfolio Tips.
520
$a
Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to deep fakes and so much more. It is crucial for aspiring data scientists or those who want to enter the field of AI to understand deep learning concepts. The best way to learn is by doing. You'll develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. You'll also work with Neural Networks and other deep learning concepts. By the end of the book, you'll have a collection of unique projects that you can add to your GitHub profiles and expand on for professional application. You will: Grasp the basic process of neural networks through projects, such as creating music Restore and colorize black and white images with deep learning processes.
630
0 0
$a
TensorFlow.
$3
864055
650
0
$a
Machine learning.
$3
188639
650
0
$a
Neural networks (Computer science)
$3
181982
650
1 4
$a
Artificial Intelligence.
$3
212515
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer Nature eBook
856
4 0
$u
https://doi.org/10.1007/978-1-4842-5802-6
950
$a
Professional and Applied Computing (SpringerNature-12059)
based on 0 review(s)
ALL
電子館藏
Items
1 records • Pages 1 •
1
Inventory Number
Location Name
Item Class
Material type
Call number
Usage Class
Loan Status
No. of reservations
Opac note
Attachments
000000188053
電子館藏
1圖書
電子書
EB Q325.5 .S581 2020 2020
一般使用(Normal)
On shelf
0
1 records • Pages 1 •
1
Multimedia
Multimedia file
https://doi.org/10.1007/978-1-4842-5802-6
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login