Neural Network Mathematics

Perceptron is nothing but it’s a type of artificial neuron. Artificial neuron is nothing but it mimics our brain as explained in my previous post.

Now we need to understand what’s the magic behind the neural network to predict any kind of output basis on the input. To get it’s better understanding, we should be knowing basic the sigmoid function and gradient decent algorithm.

I have explained the high level architecture of predicting the hand written numbers. But this time, I will be more focusing on the various types of process which comes from input to predict the output.

Below is the pictorial view of the stages of the neural network and will try to add the code in the upcoming post.

Stage 1: Define the inputs, weights, bias and label the outputs.

Stage 2: Summation of all inputs and add the bias

Stage 3: Calculate the forward pass

Stage 4: Calculate the backward pass and updates the weights

Stage 5: Repeat the process till we get the desired output.

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