東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Artificial neural networks with Java...

Livshin, Igor.

FindBook

Google Book

Amazon

博客來

Artificial neural networks with Java = tools for building neural network applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial neural networks with Java/ by Igor Livshin.
其他題名:	tools for building neural network applications /
作者:	Livshin, Igor.
出版者:	Berkeley, CA :Apress : : 2019.,
面頁冊數:	xix, 566 p. :ill., digital ;24 cm.
內容註:	Chapter 1. Learning Neural Networks -- Chapter 2. Internal Mechanism of Neural Network Processing -- Chapter 3. Manual Neural Network Processing -- Chapter 4. Java Environment and Development Tools for Building Neural Network Applications -- Chapter 5. Neural Network Development Using Java Framework -- Chapter 6. Neural network Prediction outside of the Training Range -- Chapter 7. Processing More Complex Periodic Functions -- Chapter 8. Processing Non-continuous Functions -- Chapter 9. Approximation Continuous Functions with Complex Topology -- Chapter 10. Using Neural Network for Classification of Objects -- Chapter 11. Importance of Selecting a Correct Model -- Chapter 12. Approximation of Functions in 3-D Space.
Contained By:	Springer eBooks
標題:	Neural networks (Computer science) -
電子資源:	https://doi.org/10.1007/978-1-4842-4421-0
ISBN:	9781484244210

Artificial neural networks with Java = tools for building neural network applications /
Livshin, Igor.

Artificial neural networks with Javatools for building neural network applications /[electronic resource] :by Igor Livshin. - Berkeley, CA :Apress :2019. - xix, 566 p. :ill., digital ;24 cm.

Chapter 1. Learning Neural Networks -- Chapter 2. Internal Mechanism of Neural Network Processing -- Chapter 3. Manual Neural Network Processing -- Chapter 4. Java Environment and Development Tools for Building Neural Network Applications -- Chapter 5. Neural Network Development Using Java Framework -- Chapter 6. Neural network Prediction outside of the Training Range -- Chapter 7. Processing More Complex Periodic Functions -- Chapter 8. Processing Non-continuous Functions -- Chapter 9. Approximation Continuous Functions with Complex Topology -- Chapter 10. Using Neural Network for Classification of Objects -- Chapter 11. Importance of Selecting a Correct Model -- Chapter 12. Approximation of Functions in 3-D Space.

Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of neural network processing. Artificial Neural Networks with Java also teaches you how to prepare the data to be used in neural network development and suggests various techniques of data preparation for many unconventional tasks. The next big topic discussed in the book is using Java for neural network processing. You will use the Encog Java framework and discover how to do rapid development with Encog, allowing you to create large-scale neural network applications. The book also discusses the inability of neural networks to approximate complex non-continuous functions, and it introduces the micro-batch method that solves this issue. The step-by-step approach includes plenty of examples, diagrams, and screen shots to help you grasp the concepts quickly and easily. You will: Prepare your data for many different tasks Carry out some unusual neural network tasks Create neural network to process non-continuous functions Select and improve the development model.

ISBN: 9781484244210

Standard No.: 10.1007/978-1-4842-4421-0doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Artificial neural networks with Java = tools for building neural network applications /
LDR:03014nmm a2200325 a 4500 001 2190680
003 DE-He213
005 20190412164027.0
006 m d
007 cr nn 008maaau
008 200501s2019 cau s 0 eng d
020 $a 9781484244210 $q (electronic bk.)
020 $a 9781484244203 $q (paper)
024 7 $a 10.1007/978-1-4842-4421-0 $2 doi
035 $a 978-1-4842-4421-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87
072 7 $a UMX $2 bicssc
072 7 $a COM051280 $2 bisacsh
072 7 $a UMX $2 thema
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .L788 2019
100 1 $a Livshin, Igor. $3 3409358
245 1 0 $a Artificial neural networks with Java $h [electronic resource] : $b tools for building neural network applications / $c by Igor Livshin.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a xix, 566 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1. Learning Neural Networks -- Chapter 2. Internal Mechanism of Neural Network Processing -- Chapter 3. Manual Neural Network Processing -- Chapter 4. Java Environment and Development Tools for Building Neural Network Applications -- Chapter 5. Neural Network Development Using Java Framework -- Chapter 6. Neural network Prediction outside of the Training Range -- Chapter 7. Processing More Complex Periodic Functions -- Chapter 8. Processing Non-continuous Functions -- Chapter 9. Approximation Continuous Functions with Complex Topology -- Chapter 10. Using Neural Network for Classification of Objects -- Chapter 11. Importance of Selecting a Correct Model -- Chapter 12. Approximation of Functions in 3-D Space.
520 $a Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of neural network processing. Artificial Neural Networks with Java also teaches you how to prepare the data to be used in neural network development and suggests various techniques of data preparation for many unconventional tasks. The next big topic discussed in the book is using Java for neural network processing. You will use the Encog Java framework and discover how to do rapid development with Encog, allowing you to create large-scale neural network applications. The book also discusses the inability of neural networks to approximate complex non-continuous functions, and it introduces the micro-batch method that solves this issue. The step-by-step approach includes plenty of examples, diagrams, and screen shots to help you grasp the concepts quickly and easily. You will: Prepare your data for many different tasks Carry out some unusual neural network tasks Create neural network to process non-continuous functions Select and improve the development model.
650 0 $a Neural networks (Computer science) $3 532070
650 0 $a Java (Computer program language) $3 522522
650 1 4 $a Java. $3 517732
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Open Source. $3 2210577
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-4421-0
950 $a Professional and Applied Computing (Springer-12059)