Neural Networks in Unity - ISBN: 9781484236734 - (ebook) - von Abhishek Nandy, Manisha Biswas, Verlag: Apress - Details - OvW eBook Shop

Details

Neural Networks in Unity

C# Programming for Windows 10

von: Abhishek Nandy, Manisha Biswas

26,99 €

Verlag: Apress
Format: PDF
Veröffentl.: 14.07.2018
ISBN/EAN: 9781484236734
Sprache: englisch

DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.

Beschreibungen

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.
Chapter 1:  Core Concepts of Neural NetworksChapter Goal: This chapter will walk through about neural network,will give an overview of how neural network works.No of pages 30-40Sub -Topics1. What is neural network2. Perceptron3. Single layer neural network vs multilayer perceptrons4. Activation Function and its different types5. What is Bias1. The complete neural network flowChapter 2:  Different types of Neural NetworkChapter Goal: In this chapter we will study about different types of neural network and then discuss the difference between Fully connected vs convolution neural network.No of pages: 40Sub - Topics 1. Feed forward neural network2. Radial Basis neural network3. Deep Feed Forward 4. Recurrent neural networks5. Long-Short term memory6. Auto encoder7. Hopfield Network8. Boltzmann Machine9. Restricted Boltzmann Machine10. Support Vector Machines11. What is Chain Rule?12. How to traverse a neural network13. Fully Connected versus CNNsChapter 3: Neural Network with UnityChapter Goal: No of pages: 50Sub - Topics:  1. Understanding the structure of neural network2. Creating a data structure of neural network in C#3. Creating a new project in Unity for neural network4. Getting the entire flow in Unity and finalizing the project5.  Using the Spider Asset animation for neural network6 Compiling the entire projectChapter 4: Back propagation using UnityChapter Goal: No of pages: 40Sub - Topics: 1. What is Back Propagation?2. Mathematics required for back propagation3. Getting started with Unity4. Back Propagation using Unity C#5. Completing the projectChapter 5: Neural Network with Processing and Windows 10 UWPChapter Goal: No of pages: 60Sub - Topics: 1. Getting Started with processing language2. Creating neural networks with processing3. Different simulations of neural network with processing4. Introduction to Windows 10 UWP5. Developing Neural network with processing for Windows 10 uwp.6. Making the app ready to be published in Windows Store
Abhishek Nandy is B.Tech in IT and he is a constant learner.He is Microsoft MVP at Windows Platform,Intel Black belt Developer as well as Intel Software Innovator he has keen interest on AI,IoT and Game DevelopmentCurrently serving as a Application Architect in an IT Firm as well as consulting AI,IoT as well doing projects on AI,ML and Deep learning.He also is an AI trainer and driving the technical part of Intel AI Student developer program.He was involved in the first Make in India initiative where he was among top 50 innovators and got trained in IIMA.Manisha Biswas is BTech in Information Technology and currently working as Data Scientist at Prescriber360,in kolkata, India.She is involved with several areas of technology including Web Development, IoT,Soft Computing and Artificial Intelligence.She is an Intel Software Innovator and was also awarded the SHRI DEWANG MEHTA IT AWARDS 2016 by NASSCOM,a certificate of excellence for top academic scores. She is founder of WOMEN IN TECHNOLOGY,Kolkata a tech community to empower women to learn and explore new technologies.She always like to invent things,create something new,or to invent a new look for the old things. When not in front of my terminal, She is an explorer,a traveller,a foodie, a doodler and a dreamer.She is always very passionate to share her knowledge and ideas with others.She is following her passion and doing the same currently by sharing her experiences to the community so that others can learn and give shape to her ideas in a new way this lead her to become Google Women Techmakers Kolkata Chapter Lead.
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 networksWork with Unity and C# See the difference between fully connected and convolutional neural networksMaster neural network processing for Windows 10 UWP
A great way to learn neural networks for the beginnerCovers back propagation and unsupervised neural networks with Unity C#Introduces different types of neural networks in Unity

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