國立虎尾科技大學 |

Artificial Neural Networks with TensorFlow 2 = ANN Architecture Machine Learning Projects /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Artificial Neural Networks with TensorFlow 2/ by Poornachandra Sarang.
Reminder of title:	ANN Architecture Machine Learning Projects /
Author:	Sarang, Poornachandra.
Description:	XXIX, 726 p. 237 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-6150-7
ISBN:	9781484261507

Artificial Neural Networks with TensorFlow 2 = ANN Architecture Machine Learning Projects /
Sarang, Poornachandra.

Artificial Neural Networks with TensorFlow 2ANN Architecture Machine Learning Projects /[electronic resource] :by Poornachandra Sarang. - 1st ed. 2021. - XXIX, 726 p. 237 illus.online resource.

Chapter 1: TensorFlow Jump Start -- Chapter 2: A Closer Look at TensorFlow -- Chapter 3: Deep Dive in tf.keras -- Chapter 4: Transfer Learning -- Chapter 5: Neutral Networks for Regression -- Chapter 6: Estimators -- Chapter 7: Text Generation -- Chapter 8: Language Translation -- Chapter 9: Natural Langauge -- Chapter 10: Image Captioning -- Chapter 11: Time Series -- Chapter 12: Style Transfer -- Chapter 13: Image Generation- Chapter 14: Image Translation.

Develop machine learning models across various domains. This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects. After learning what's new in TensorFlow 2, you'll dive right into developing machine learning models through applicable projects. This book covers a wide variety of ANN architectures—starting from working with a simple sequential network to advanced CNN, RNN, LSTM, DCGAN, and so on. A full chapter is devoted to each kind of network and each chapter consists of a full project describing the network architecture used, the theory behind that architecture, what data set is used, the pre-processing of data, model training, testing and performance optimizations, and analysis. This practical approach can either be used from the beginning through to the end or, if you're already familiar with basic ML models, you can dive right into the application that interests you. Line-by-line explanations on major code segments help to fill in the details as you work and the entire project source is available to you online for learning and further experimentation. With Artificial Neural Networks with TensorFlow 2 you'll see just how wide the range of TensorFlow's capabilities are. You will: Develop Machine Learning Applications Translate languages using neural networks Compose images with style transfer.

ISBN: 9781484261507

Standard No.: 10.1007/978-1-4842-6150-7doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Artificial Neural Networks with TensorFlow 2 = ANN Architecture Machine Learning Projects /
LDR:03245nam a22003975i 4500 001 1049204
003 DE-He213
005 20210622085651.0
007 cr nn 008mamaa
008 220103s2021 xxu| s |||| 0|eng d
020 $a 9781484261507 $9 978-1-4842-6150-7
024 7 $a 10.1007/978-1-4842-6150-7 $2 doi
035 $a 978-1-4842-6150-7
050 4 $a Q325.5-.7
050 4 $a TK7882.P3
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
100 1 $a Sarang, Poornachandra. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1353267
245 1 0 $a Artificial Neural Networks with TensorFlow 2 $h [electronic resource] : $b ANN Architecture Machine Learning Projects / $c by Poornachandra Sarang.
250 $a 1st ed. 2021.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a XXIX, 726 p. 237 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
505 0 $a Chapter 1: TensorFlow Jump Start -- Chapter 2: A Closer Look at TensorFlow -- Chapter 3: Deep Dive in tf.keras -- Chapter 4: Transfer Learning -- Chapter 5: Neutral Networks for Regression -- Chapter 6: Estimators -- Chapter 7: Text Generation -- Chapter 8: Language Translation -- Chapter 9: Natural Langauge -- Chapter 10: Image Captioning -- Chapter 11: Time Series -- Chapter 12: Style Transfer -- Chapter 13: Image Generation- Chapter 14: Image Translation.
520 $a Develop machine learning models across various domains. This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects. After learning what's new in TensorFlow 2, you'll dive right into developing machine learning models through applicable projects. This book covers a wide variety of ANN architectures—starting from working with a simple sequential network to advanced CNN, RNN, LSTM, DCGAN, and so on. A full chapter is devoted to each kind of network and each chapter consists of a full project describing the network architecture used, the theory behind that architecture, what data set is used, the pre-processing of data, model training, testing and performance optimizations, and analysis. This practical approach can either be used from the beginning through to the end or, if you're already familiar with basic ML models, you can dive right into the application that interests you. Line-by-line explanations on major code segments help to fill in the details as you work and the entire project source is available to you online for learning and further experimentation. With Artificial Neural Networks with TensorFlow 2 you'll see just how wide the range of TensorFlow's capabilities are. You will: Develop Machine Learning Applications Translate languages using neural networks Compose images with style transfer.
650 0 $a Machine learning. $3 561253
650 1 4 $a Machine Learning. $3 1137723
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484261491
776 0 8 $i Printed edition: $z 9781484261514
856 4 0 $u https://doi.org/10.1007/978-1-4842-6150-7
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)