東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Learn Keras for deep neural networks...

Moolayil, Jojo.

Linked to FindBook

Google Book

Amazon

博客來

Learn Keras for deep neural networks = a fast-track approach to modern deep learning with Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learn Keras for deep neural networks/ by Jojo Moolayil.
Reminder of title:	a fast-track approach to modern deep learning with Python /
Author:	Moolayil, Jojo.
Published:	Berkeley, CA :Apress : : 2019.,
Description:	xv, 182 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Deep Learning & Keras -- Chapter 2: Keras in Action -- Chapter 3: Deep Neural networks for Supervised Learning -- Chapter 4: Measuring Performance for DNN -- Chapter 5: Hyperparameter Tuning & Model Deployment -- Chapter 6: The Path Forward.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-4240-7
ISBN:	9781484242407

Learn Keras for deep neural networks = a fast-track approach to modern deep learning with Python /
Moolayil, Jojo.

Learn Keras for deep neural networksa fast-track approach to modern deep learning with Python /[electronic resource] :by Jojo Moolayil. - Berkeley, CA :Apress :2019. - xv, 182 p. :ill., digital ;24 cm.

Chapter 1: Deep Learning & Keras -- Chapter 2: Keras in Action -- Chapter 3: Deep Neural networks for Supervised Learning -- Chapter 4: Measuring Performance for DNN -- Chapter 5: Hyperparameter Tuning & Model Deployment -- Chapter 6: The Path Forward.

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras. You will: Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks.

ISBN: 9781484242407

Standard No.: 10.1007/978-1-4842-4240-7doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .M665 2019

Dewey Class. No.: 006.31

Learn Keras for deep neural networks = a fast-track approach to modern deep learning with Python /
LDR:03118nmm a2200325 a 4500 001 2179136
003 DE-He213
005 20190719103315.0
006 m d
007 cr nn 008maaau
008 191122s2019 cau s 0 eng d
020 $a 9781484242407 $q (electronic bk.)
020 $a 9781484242391 $q (paper)
024 7 $a 10.1007/978-1-4842-4240-7 $2 doi
035 $a 978-1-4842-4240-7
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .M665 2019
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 .M817 2019
100 1 $a Moolayil, Jojo. $3 3383983
245 0 0 $a Learn Keras for deep neural networks $h [electronic resource] : $b a fast-track approach to modern deep learning with Python / $c by Jojo Moolayil.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a xv, 182 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Deep Learning & Keras -- Chapter 2: Keras in Action -- Chapter 3: Deep Neural networks for Supervised Learning -- Chapter 4: Measuring Performance for DNN -- Chapter 5: Hyperparameter Tuning & Model Deployment -- Chapter 6: The Path Forward.
520 $a Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras. You will: Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks.
650 0 $a Machine learning. $3 533906
650 0 $a Neural networks (Computer science) $3 532070
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Open Source. $3 2210577
650 2 4 $a Python. $3 3201289
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-4240-7
950 $a Professional and Applied Computing (Springer-12059)