Hi , Before going to AI , just see the behavior of Data ( linear or Non linear ) , Just see the enough data is available for Training . Good Luck .

Step 1: Neurons and forward propagation

So what is a neural network? Let’s wait with the network part and start off with one single neuron.

A neuron is like a function; it takes a few inputs and calculates an output.

The circle below illustrates an artificial neuron. Its input is 5 and its output is 1. The input is the sum of the three synapses connecting to the neuron (the three arrows at the left).

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At the far left we see two input values plus a bias value. The input values are 1 and 0 (the green numbers), while the bias holds a value of -2 (the brown number).

The inputs here might be numerical representations of two different features. If we’re building a spam filter, it could be whether or not the email contains more than one CAPITALIZED WORD and whether or not it contains the word ‘viagra’.

The two inputs are then multiplied by their so called weights, which are 7 and 3 (the blue numbers).

Finally we add it up with the bias and end up with a number, in this case: 5 (the red number). This is the input for our artificial neuron.

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The neuron then performs some kind of computation on this number — in our case the Sigmoid function, and then spits out an output. This happens to be 1, as Sigmoid of 5 equals to 1, if we round the number up (more info on the Sigmoid function follows later).

If this was a spam filter, the fact that we’re outputting 1 (as opposed to 0) probably means that the neuron has labeled the text as ‘spam’.

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