Arduino Basics

The Layer The purpose of a layer is mainly to group and manage neurons that are functionally similar, and provide a means to effectively control the flow of information through the network (forwards and backwards). The forward pass is when you convert input values into output values, the backwards pass is only done during network training. In my neural network, the Layer is an essential building block of the neural network. They can work individually or as a team. Let us just focus on one layer for now This is what a layer with two neurons looks like in my neural network: As you can see from the picture above, I tend to treat each neuron as an individual. Both neurons have the same connEntry values ( cEn11 = cEn21 ) and ( cEn12 = cEn22 ), which stem from the layerINPUTs. Other than that, the rest is different.  Which means that Neuron1 could produce an output value close to 1, and at the same time Neuron2 could produce an output close to 0, e...

The Neuron The main purpose of the Neuron is to store the values that flow through the neural network. They also do a bit of processing through the use of an Activation function. The activation function is quite an important feature of the neural network. It essentially enables the neuron to make decisions (yes/no and greyzone) based on the values provided to them. The other feature of activation functions is the ability to map values of infinite size to a range of 0 to 1. We will be using the Sigmoid function . The above pictures can be found at Wikipedia The neuron accumulates signals from one or more connections, adds a bias value, and then sends this value through the activation function to produce a neuron output. Each neuron output will be within the range of 0 to 1 (due to the activation function). So lets take a look at  a single neuron with 2 connections (+ bias) feeding into it. As seen below. connEntry      ...