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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

Time Series Prediction and LSTM Using CNTK

This chapter is dedicated to helping you understand more of the Microsoft Cognitive Toolkit, or CNTK. The inspiration for the examples contained within this chapter comes from the Python version of CNTK 106: Part A – Time Series prediction with LSTM (Basics). As C# developers, the Python code is not what we will be using (although there are several ways in which we could) so we made our own C# example to mirror that tutorial. To make our example easy and intuitive, we will use the Sine function to predict future time-series data. Specifically, and more concretely, we will be using a long short-term memory recurrent neural network, sometimes called an LSTM-RNN or just LSTM. There are many variants of the LSTM; we will be working with the original.

In this chapter, we will cover the following topics:

  • LSTM
  • Tensors
  • Static and dynamic...
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