Neural Networks in Plain English (Part 2)

Ankit
Ankit
Aug 25, 2017 · 2 min read

This is second part of series , you can find first part here , continuing from where I left , in this article I will explain what is training , and how did I assume 1 as threshold in last article

When we are born , we don’t know things and people around us , our parents , teacher shows example like hey look this is a cat and we first time look at a cat and then understand that cat looks like this , we are given lots of example of how a cat looks and how it is different from other animals . And finally we learn what a cat is and we can easily look at animal and tell whether it is a cat or not . This may look like a simple phenomena .But our brain gets trained multiple times to learn how a cat looks . This is called training

In last article I showed my love for South Indian Foods , this means from my childhood I have eaten different types of food and slowly developed likeness for South Indian food . Technically my brain is trained now , I have seen and eaten South Indian Food many times , that’s why my brain gave higher preference to food type .

This is the exact idea behind perceptron.

In a similar manner when we are building artificial neural network , inputs are given to perceptron one after another and weights get modified according to this equation

For all inputs i,

W(i) = W(i) + l*(Correct Output - Output given by perceptron)*Input(i), where l is the learning rate

I have written whole name equation to make sure any one does not get confused by variable names . This is a very important equation .

So , what is happening ?

Perceptron is adding all the inputs and separating them into 2 categories, those that cause it to fire and those that don’t.

w1x1 + w2x2 = t, where t is the threshold

This is it , in next article I will explain you about what is back propagation and how it is useful .

Thank you for reading .

You can connect me with me in Linkedin

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