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To be a little more specific our network will consist of neurons and perceptrons which will feed-forward and use backpropagation to learn. This may sound alien to you but fear not, I will explain everything in detail. It does require some familiarity with Object-oriented programming OOP. We will use the popular MNIST data set of handwritten digits to train the network and ultimately recognise each of them.

Some of the neurons connected to your visual cortex would get excited and pass the signal to other neurons through a networks of dendrites which releases a chemical signal. The photons are therefore processed through a number of layers leading to a correct action, in this instance reducing your speed.

Neural Networks In C++

Finally, this hidden layer connects to an output layer, the equivalent to the motor neurons which caused you to slow down. Notice how every neuron within each layer is not linked to one another unlike in our brain. Rather, they are linked to all the neurons in the following layer.

This explains the feed-forward nature of our network, data is passed through the layers in a forward manner. Regarding the size of the matrices in each layer they have been kept as small as possible so the network can be run on any laptop. Each digit input is represented as a grid of 14x14 pixels see below which was downsized from 28x28 by Alasdair Turner.

The hidden layer consist of a grid of 7x7, this is a completely arbitrary choice and I encourage you to play around with this once you have everything running.

Lastly, our output is a series of 10 neurons each one indicating a possible digit from 0 to 9. Instead of having neurons in each layer we will also have Perceptrons. Notice how each input is weighted, the ones with a higher number bold arrow signify a stronger link between the neurons.

Live Coding 2-0: Neural Network in C++ (Intro + Neuron)

Loading the data. The first thing we need to do is to load the data and display it as a grid. Unlike our brain they will stay in whatever state they were last set to. The full data set contains 10, written digits, we will split this in two so we have a training set of 8, digits and a testing set of 2, digits.

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