Introduction

Welcome to our riveting exploration of "Understanding Activation Functions". We'll traverse the realm of activation functions — crucial components in neural networks that guide the network's output. Embark with us as we delve deep into the theory and Python implementations of five specific activation functions:

  • step function,
  • sigmoid function,
  • Rectified Linear Unit (ReLU),
  • hyperbolic tangent (tanh)
  • softplus function. Let's embark on this enlightening journey through the realm of neural networks.
Theoretical Understanding of Activation Functions

Let's unravel the role of activation functions in neural networks. They play a vital part in determining the neuron's output. Picturing them as computational gates can be helpful: these gates output results if the input crosses a threshold; otherwise, they remain silent. As we embark on our journey, we'll explore five types of activation functions listed above.

Step Function Implementation

At the start of our expedition, let's explore the step function, also known as the threshold function. This basic activation function works like a switch. If the input value is above or equal to a threshold value, the function returns 1; otherwise, it returns 0.

Implementing this in Python is straightforward due to its unique characteristic:

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