## What are the building blocks?

Deep learning is based in Neural Networks, but what are the underlying blocks of NN?

### Perceptrons

The most basic part of a Neural Network is called Perceptron. But how does it work?

If you go back to your high school math class, maybe you can remember that piece of equation for a line, y = ax + b. The percetron does nothing more than that, it’s a adder, it takes x1 and multiplies by w1, takes x2 and multiplies by w2 and sum all the pieces together with the bias. The final result is the output of the percetron.

### Activation functions

The output of the perceptron can be any value, if we want to work with probabilities of events happening, we need to adjust the outputs to values between 0 and 1. Here the activation functions take place, we can use sigmoid functions, where the result is always between 0 and 1, we can also use step functions.

### Summary:

- Perceptrons are the building blocks of Neural Networks.
- They are linear classifiers.
- We can use activation functions to adjust the outputs.

## What are the pitfalls of perceptrons?

As Perceptrons are linear classifiers, by themselves they can’t solve non-linear problems. We group them by layers so they can’t tackle a lot of more complex problems, and this is how Neural Networks are born! If you want more content like this, I have a YouTube Channel