Yesterday, I was considering going for *The Most Epic Action Movie Ever*. Since it was an action movie, I was up right away. A quick search online revealed it having good ratings. That’s great! I called my friends, informed my plans and they jumped in. All boxes ticked, so I went.

The decision process can be visualized as below.

Here is how I made the decision:

It’s an action movie (+1 point) with a good rating (+1 point) and my friends are coming (+1 point). Total = +3

Now, my decision parameter is if I get a total of 3 points then I will go, else I won’t.

```
(1) going if points = 3
(0) not going if points < 3
```

It is possible that I am ok with meeting just two of the criteria. In which case:

```
(1) going if points >= 2
(0) not going if points < 2
```

This decision parameter is called an ** activation function**. I am going to represent

If I am limited by budget then,

Here, I have introduced a penalty. Suppose the movie tickets do not fit my budget but all else holds true. I will calculate my points as:

Since 1 < 2 I will not be going for the movie.

These numbers on the edges between the nodes are called **weights**. So far I have covered weights and inputs. Let’s try to write this in a generalized form.

Let x_{1} denote rating input, x_{2} be action input and so on.

Let w_{1} denote the weight between rating and output node. w_{2} between action and output and so on.

The equation then becomes:

This can be written as \( \sum_{i=1} ^4 x_i w_i \)

Let f(x) be the activation function. Then the whole equation can be represented as:

\( f(\sum_{i=1} ^4 x_i w_i) \) However, there is another term remaining called bias.

**Bias** is

This can be interpreted

The general function now becomes:

This is the general architecture of an artificial neuron. There are many different architectures of artificial neural networks such as RNNs, Feed-forward and Convolutional NN. Even different varieties of activation functions haven’t been explored. And, concepts such as back-propagation, momentum and dropouts haven’t been touched. In the following posts, I am going to cover all these concepts.

Hey! You have reached the end ðŸ˜Ž. Thanks for reading

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