A hyperbolic tangent function is another type of activation function used in Deep Learning, which is a smoother, zero-centred function.

x: an input data point

In terms of sigmoid function:

Range: (-1, 1)

```
# Import Numpy Library
import numpy as np
#Hyperbolic Tangent Function
def tanh(x):
return 2*(sigmoid(2*x)) - 1
```

Use cases:

Used in multi-layered neural networks.

Mostly appears in recurrent neural networks for natural language processing and speech recognition.

Pros:

Tanh function gives better results than sigmoid function when dealing with multi-layered neural networks.

Since the range of Tanh function is (-1, 1), the outputs are zero centered and having a higher convergence, the network works on normalized data resulting in faster learning.

Cons:

Though Tanh is a zero centered function, it still suffers from vanishing gradient during backpropagation.

Produces some dead neurons when the input value is 0.

Hard Hyperbolic function is a variant of Tanh function, which reduces the computational speed thus aids in NLP applications.