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BL
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| Record Number
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851087
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| Main Entry
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Nandy, Abhishek
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| Title & Author
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Neural Networks in Unity : : C# Programming for Windows 10 /\ Abhishek Nandy, Manisha Biswas.
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| Publication Statement
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California :: Apress,, [2018]
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, ©2018
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| Page. NO
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1 online resource
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| ISBN
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1484236734
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: 9781484236734
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1484236726
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9781484236727
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| Notes
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Includes index.
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| Contents
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Intro; Table of Contents; About the Authors; About the Technical Reviewer; Introduction; Chapter 1: Neural Network Basics; Introducing Neural Networks; Digging Deeper into Neural Networks; Perceptron; Activation Function andIts Different Types; Identity Function; Binary Step Function; Logistic or Sigmoid; Tan H Function; Arctan Function; Rectified Linear Unit; Leaky ReLU; Softmax Function; Biases andWeights; Neural Network fromScratch; Backpropagation; Summary; Chapter 2: Unity ML-Agents; Unity IDE; Getting Started withMachine Learning Agents; Let's Start withTensorFlow
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Backpropogation inUnity C#Constructing Data Structures; Feed Forwarding andInitializing Weights; Testing of Backpropagation Neural Network; Summary; Chapter 5: Data Visualization inUnity; Machine Learning Data Visualization inUnity; Data Parsing; Working withDatasets; Another Example; Summary; Index
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Understanding AnacondaWhat Is theNVDIA CUDA Toolkit?; GPU-Accelerated TensorFlow; Building aProject inUnity; Internal Operations forMachine Learning; Training Anaconda inPython Mode; Working withJupyter Notebook; Proximity Policy Optimization; Summary; Chapter 3: Machine Learning Agents andNeural Network inUnity; Extending theUnity ML-Agents withFurther Examples; Crawler Project; Testing theSimulation; Neural Network withUnity C#; Creating DataStructures; Experimenting withtheSpider Asset; Summary; Chapter 4: Backpropagation inUnity C#; Going Further into Backpropagation
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| Abstract
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"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. What You'll LearnDiscover the concepts behind neural networksWork with Unity and C#See the difference between fully connected and convolutional neural networksMaster neural network processing for Windows 10 UWPWho This Book Is ForGaming professionals, machine learning and deep learning enthusiasts."--
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| Subject
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C# (Computer program language)
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Neural networks (Computer science)-- Computer programs.
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C# (Computer program language)
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COMPUTERS-- General.
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Games development programming.
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Microsoft programming.
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| Subject
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Unity (Electronic resource)
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Unity (Electronic resource)
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| Dewey Classification
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006.3/2
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| LC Classification
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QA76.87
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| Added Entry
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Biswas, Manisha
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