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Hands-On Neural Network Programming with C#

You're reading from   Hands-On Neural Network Programming with C# Add powerful neural network capabilities to your C# enterprise applications

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781789612011
Length 328 pages
Edition 1st Edition
Languages
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Author (1):
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Matt Cole Matt Cole
Author Profile Icon Matt Cole
Matt Cole
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Table of Contents (16) Chapters Close

Preface 1. A Quick Refresher FREE CHAPTER 2. Building Our First Neural Network Together 3. Decision Trees and Random Forests 4. Face and Motion Detection 5. Training CNNs Using ConvNetSharp 6. Training Autoencoders Using RNNSharp 7. Replacing Back Propagation with PSO 8. Function Optimizations: How and Why 9. Finding Optimal Parameters 10. Object Detection with TensorFlowSharp 11. Time Series Prediction and LSTM Using CNTK 12. GRUs Compared to LSTMs, RNNs, and Feedforward networks 13. Activation Function Timings
14. Function Optimization Reference 15. Other Books You May Enjoy

Creating your own autoencoder

Now that you are an expert on autoencoders, let's move on to less theory and more practice. Let's take a bit of a different route on this one. Instead of using an open-source package and showing you how to use it, let's write our own autoencoder framework that you can enhance to make your own. We'll discuss and implement the basic pieces needed, and then write some sample code showing how to use it. We will make this chapter unique in that we won't finish the usage sample; we'll do just enough to get you started along your own path to autoencoder creation. With that in mind, let's begin.

Let's start off by thinking about what an autoencoder is and what things we would want to include. First off, we're going to need to keep track of the number of layers that we have. These layers will be Restricted Boltzmann...

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