國立虎尾科技大學 |

Neural Networks in Unity = C# Programming for Windows 10 /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Neural Networks in Unity/ by Abhishek Nandy, Manisha Biswas.
Reminder of title:	C# Programming for Windows 10 /
Author:	Nandy, Abhishek.
other author:	Biswas, Manisha.
Description:	XI, 158 p. 107 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computer games—Programming. -
Online resource:	https://doi.org/10.1007/978-1-4842-3673-4
ISBN:	9781484236734

Neural Networks in Unity = C# Programming for Windows 10 /
Nandy, Abhishek.

Neural Networks in UnityC# Programming for Windows 10 /[electronic resource] :by Abhishek Nandy, Manisha Biswas. - 1st ed. 2018. - XI, 158 p. 107 illus.online resource.

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

Standard No.: 10.1007/978-1-4842-3673-4doiSubjects--Topical Terms:

1256819
Computer games—Programming.

LC Class. No.: QA76.76.C672

Dewey Class. No.: 794.815

Neural Networks in Unity = C# Programming for Windows 10 /
LDR:02629nam a22003975i 4500 001 986346
003 DE-He213
005 20200703052330.0
007 cr nn 008mamaa
008 201225s2018 xxu| s |||| 0|eng d
020 $a 9781484236734 $9 978-1-4842-3673-4
024 7 $a 10.1007/978-1-4842-3673-4 $2 doi
035 $a 978-1-4842-3673-4
050 4 $a QA76.76.C672
072 7 $a UMK $2 bicssc
072 7 $a COM012040 $2 bisacsh
072 7 $a UMK $2 thema
082 0 4 $a 794.815 $2 23
100 1 $a Nandy, Abhishek. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1115992
245 1 0 $a Neural Networks in Unity $h [electronic resource] : $b C# Programming for Windows 10 / $c by Abhishek Nandy, Manisha Biswas.
250 $a 1st ed. 2018.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2018.
300 $a XI, 158 p. 107 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: 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.
650 0 $a Computer games—Programming. $3 1256819
650 0 $a Microsoft software. $3 1253736
650 0 $a Microsoft .NET Framework. $3 565417
650 1 4 $a Game Development. $3 1114505
650 2 4 $a Microsoft and .NET. $3 1114109
700 1 $a Biswas, Manisha. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1207889
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484236727
776 0 8 $i Printed edition: $z 9781484236741
776 0 8 $i Printed edition: $z 9781484247365
856 4 0 $u https://doi.org/10.1007/978-1-4842-3673-4
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)