About Neural Networks - Briefly explained
What is a Neural Network?
A neural network is often referred to and used within the field of Artificial Intelligence.
It consists of a series of algorithms with the aim of recognising the underlying relationships in a dataset. This is done using a process, that is designed to mimic the same way as a human brain operates, as closely as is possible. Neural networks therefore refer to organic or artificial systems of neurons.
How they work
In its simplest form, a neural network consists of an input layer, an output layer (also known as a target layer) and, in between, there is a hidden layer. The layers are then connected via nodes, which connected together then form the neural network.
The Benefits of a Neural Network
They can implement tasks that a linear program cannot.
When part of a neural network declines, it can continue using parallel features.
Neural networks determine themselves and do not need to be re-programmed.
They can be executed in almost any application.
Types of neural networks
There are three main types, that form the basis for most deep learning models:
Artificial Neural Networks (ANN)
Convolution Neural Networks (CNN)
Recurrent Neural Networks (RNN)
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Further detailed Reading:
How to build a fast Visual Recognition Memory System using AI
Deep Learning Embedded systems and its benefits
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