Book Image

Hands-On Neural Network Programming with C#

By : Matt Cole
Book Image

Hands-On Neural Network Programming with C#

By: Matt Cole

Overview of this book

Neural networks have made a surprise comeback in the last few years and have brought tremendous innovation in the world of artificial intelligence. The goal of this book is to provide C# programmers with practical guidance in solving complex computational challenges using neural networks and C# libraries such as CNTK, and TensorFlowSharp. This book will take you on a step-by-step practical journey, covering everything from the mathematical and theoretical aspects of neural networks, to building your own deep neural networks into your applications with the C# and .NET frameworks. This book begins by giving you a quick refresher of neural networks. You will learn how to build a neural network from scratch using packages such as Encog, Aforge, and Accord. You will learn about various concepts and techniques, such as deep networks, perceptrons, optimization algorithms, convolutional networks, and autoencoders. You will learn ways to add intelligent features to your .NET apps, such as facial and motion detection, object detection and labeling, language understanding, knowledge, and intelligent search. Throughout this book, you will be working on interesting demonstrations that will make it easier to implement complex neural networks in your enterprise applications.
Table of Contents (16 chapters)
13
Activation Function Timings

GRUs Compared to LSTMs, RNNs, and Feedforward networks

In this chapter, we're going to talk about gated recurrent units (GRU). We will also compare them to LSTMs, which we learned about in the previous chapter. As you know, LSTMs have been around since 1987 and are among the most widely used models in Deep Learning for NLP today. GRUs, however, were first presented in 2014, are a simpler variant of LSTMs that share many of the same properties, train easier and faster, and typically have less computational complexity.

In this chapter, we will learn about the following:

  • GRUs
  • How GRUs differ from LSTMs
  • How to implement a GRU
  • GRU, LTSM, RNN, and Feedforward comparisons
  • Network differences