Disruptive technology explained – Artificial Neural Network & Deep Learning

Disruptive technology explained – Artificial Neural Network & Deep Learning

What is an Artificial Neural Network? 

The human brain is a neural network; it consists of millions of nerve cells (neurons) which take in inputs and send outputs, via synapses, to other nerve cells. This allows the brain to make predictions, or carry out functions, such as breathing, or holding your breath when under water. The most important element of a neural network is that it learns. It starts out with a simple premise and some inputs, and continually tests the outputs against reality, amending the way it processes data, until it is able to make predictions with a high degree of accuracy.

An artificial neural network, is a computer representation of the human brain. It has inputs and outputs, and in the middle, one or more ‘hidden’ layers, that act as neurons making decisions.

Both natural and artificial neural networks can learn in a number of ways: supervised, unsupervised or by reinforcement learning; whichever method is used the network ends up being better at making decisions than it started.


What is Deep Learning?

Deep learning uses neural networks with many hidden layers, i.e. those which lie between the input and output layers. It allows a large number of variables to be used as inputs with feedback, allowing the network to learn how those variables interact in different scenarios, and then to develop a weighting and bias for each, in the same way a human would, but with the advantage of being able to accurately consider many more than a person could.



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