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Titlebook: Neural Networks in Unity; C# Programming for W Abhishek Nandy,Manisha Biswas Book 2018 Abhishek Nandy, Manisha Biswas 2018 Unity.Artificial

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发表于 2025-3-21 18:31:46 | 显示全部楼层 |阅读模式
书目名称Neural Networks in Unity
副标题C# Programming for W
编辑Abhishek Nandy,Manisha Biswas
视频video
概述A great way to learn neural networks for the beginner.Covers back propagation and unsupervised neural networks with Unity C#.Introduces different types of neural networks in Unity
图书封面Titlebook: Neural Networks in Unity; C# Programming for W Abhishek Nandy,Manisha Biswas Book 2018 Abhishek Nandy, Manisha Biswas 2018 Unity.Artificial
描述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 Learn.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.Who This Book Is For.Gaming professionals, machine learning and deep learning enthusiasts..
出版日期Book 2018
关键词Unity; Artificial intelligence; Neural Networks; Back Propogation; C#; Conventional Neural Networks
版次1
doihttps://doi.org/10.1007/978-1-4842-3673-4
isbn_softcover978-1-4842-3672-7
isbn_ebook978-1-4842-3673-4
copyrightAbhishek Nandy, Manisha Biswas 2018
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Abhishek Nandy,Manisha Biswashighly resolved .Si NMR spectra of microcrystalline or amorphous solids. In proton containing samples, line broadening due to dipolar .Si-.H interactions can be removed by high power proton decoupling, and cross-polarization (CP) may be used for signal enhancement and the detection of protons in clo
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Abhishek Nandy,Manisha Biswasecules is presented. It is shown that the side-group motion in glassy poly(methyl methacrylate) is a rather complex process involving a coupling to a rocking motion of the main chain. Finally it is demonstrated that 2D exchange NMR can also be applied to determine the orientational distribution func
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Abhishek Nandy,Manisha Biswasctrum, through judicious control of time-dependent radiofrequency (..) fields and/or manipulation of sample orientation with respect to a large static field (..). In low-viscosity liquids, fast isotropic molecular motion averages all anisotropic interactions, so that narrow line widths are readily o
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ctrum, through judicious control of time-dependent radiofrequency (..) fields and/or manipulation of sample orientation with respect to a large static field (..). In low-viscosity liquids, fast isotropic molecular motion averages all anisotropic interactions, so that narrow line widths are readily o
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