國立高雄大學圖資館 |

Language: English

Back

Neural networks in UnityC# programmi...

Biswas, Manisha.

Neural networks in UnityC# programming for Windows 10 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neural networks in Unityby Abhishek Nandy, Manisha Biswas.
Reminder of title:	C# programming for Windows 10 /
Author:	Nandy, Abhishek.
other author:	Biswas, Manisha.
Published:	Berkeley, CA :Apress :2018.
Description:	xi, 158 p. :digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Neural networks (Computer science)Computer programs.
Online resource:	http://dx.doi.org/10.1007/978-1-4842-3673-4
ISBN:	9781484236734$q(electronic bk.)

Neural networks in UnityC# programming for Windows 10 /
Nandy, Abhishek.

Neural networks in UnityC# programming for Windows 10 /[electronic resource] :by Abhishek Nandy, Manisha Biswas. - Berkeley, CA :Apress :2018. - xi, 158 p. :digital ;24 cm.

Chapter 1: Core Concepts of Neural Networks -- Chapter 2: Different types of Neural Network -- Chapter 3: Neural Network with Unity -- Chapter 4: Back propagation using Unity -- Chapter 5: Neural Network with Processing and Windows 10 UWP.

Learn the core concepts of neural networks and discover the different types of neural network, using Unity as your platform. In this book you will start by exploring back propagation and unsupervised neural networks with Unity and C#. You'll then move onto activation functions, such as sigmoid functions, step functions, and so on. The author also explains all the variations of neural networks such as feed forward, recurrent, and radial. Once you've gained the basics, you'll start programming Unity with C#. In this section the author discusses constructing neural networks for unsupervised learning, representing a neural network in terms of data structures in C#, and replicating a neural network in Unity as a simulation. Finally, you'll define back propagation with Unity C#, before compiling your project. You will: Discover the concepts behind neural networks Work with Unity and C# See the difference between fully connected and convolutional neural networks Master neural network processing for Windows 10 UWP.

ISBN: 9781484236734$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-3673-4doiSubjects--Uniform Titles:

Unity (Electronic resource)
Subjects--Topical Terms:

821465
Neural networks (Computer science)
--Computer programs.

LC Class. No.: QA76.87 / .N363 2018

Dewey Class. No.: 006.32

Neural networks in UnityC# programming for Windows 10 /
LDR:02209nmm a2200301 a 4500 001 543254
003 DE-He213
005 20190213151257.0
006 m d
007 cr nn 008maaau
008 190411s2018 cau s 0 eng d
020 $a 9781484236734$q(electronic bk.)
020 $a 9781484236727$q(paper)
021 7 3 $a nam a2200289 a 4500
024 7 $a 10.1007/978-1-4842-3673-4 $2 doi
035 $a 978-1-4842-3673-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87 $b .N363 2018
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .N176 2018
100 1 $a Nandy, Abhishek. $3 763352
245 1 0 $a Neural networks in Unity $h [electronic resource] : $b C# programming for Windows 10 / $c by Abhishek Nandy, Manisha Biswas.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2018.
300 $a xi, 158 p. : $b digital ; $c 24 cm.
505 0 $a Chapter 1: Core Concepts of Neural Networks -- Chapter 2: Different types of Neural Network -- Chapter 3: Neural Network with Unity -- Chapter 4: Back propagation using Unity -- Chapter 5: Neural Network with Processing and Windows 10 UWP.
520 $a Learn the core concepts of neural networks and discover the different types of neural network, using Unity as your platform. In this book you will start by exploring back propagation and unsupervised neural networks with Unity and C#. You'll then move onto activation functions, such as sigmoid functions, step functions, and so on. The author also explains all the variations of neural networks such as feed forward, recurrent, and radial. Once you've gained the basics, you'll start programming Unity with C#. In this section the author discusses constructing neural networks for unsupervised learning, representing a neural network in terms of data structures in C#, and replicating a neural network in Unity as a simulation. Finally, you'll define back propagation with Unity C#, before compiling your project. You will: Discover the concepts behind neural networks Work with Unity and C# See the difference between fully connected and convolutional neural networks Master neural network processing for Windows 10 UWP.
630 0 0 $a Unity (Electronic resource) $3 524921
650 0 $a Neural networks (Computer science) $x Computer programs. $3 821465
650 0 $a C# (Computer program language) $3 215229
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Game Development. $3 761231
650 2 4 $a Microsoft and .NET. $3 760507
700 1 $a Biswas, Manisha. $3 803945
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-1-4842-3673-4
950 $a Professional and Applied Computing (Springer-12059)