# Data Science - Functions

## Softmax

This model generalizes logistic regression to classification problems where the class label y can take on more than two possible values.

## Sigmoid

Sigmod: "S"-shaped curve

logistic sigmoid, squashing function=>maps the whole real axis into a finite interval inverse: logit function: log odds

- logistic: https://en.wikipedia.org/wiki/Logistic_function
- sigmoid: https://en.wikipedia.org/wiki/Sigmoid_function

```
def sigmoid(z):
return 1.0 / (1 + np.exp(-z))
```

### Sigmoid vs Logistic

Logistic is one kind of Sigmoid function(s-curve)

### Sigmoid vs Tanh

- tanh: y in
`[-1,1]`

- sigmoid: y in
`[0,1]`

## Rectifier

$f(x) = max(0, x)$also known as a ramp function and is analogous to half-wave rectification in electrical engineering.

A unit employing the rectifier is also called a rectified linear unit (ReLU)

A smooth approximation to the rectifier is the analytic function, or softplus function

$f(x) = ln(1 + e^x)$The derivative of softplus is logistic function.

$f(x) = e^x/(e^x + 1) = 1 / (1 + e^-x)$A unit employing the rectifier is also called a rectified linear unit (ReLU).

https://en.wikipedia.org/wiki/Rectifier*(neural*networks)

## tanh

wiki: https://en.wikipedia.org/wiki/Hyperbolic_function

tanh activation function is nothing but $2*\verb sigmoid - 1$

$tanh(x)=2σ(2x)−1$There are two reasons for that choice(tanh) (assuming you have normalized your data, and this is very important):

- Having stronger gradients: since data is centered around 0, the derivatives are higher. To see this, calculate the derivative of the tanh function and notice that input values are in the range
`[0,1]`

. - Avoiding bias in the gradients. This is explained very well in the paper, and it is worth reading it to understand these issues.