Activation functions in Neural Networks. relu, softmax ,sigmod, binary step function.

Activation functions are a crucial component of neural networks, playing a vital role in determining the network's output. In essence, an activation function is a mathematical equation that defines how the input of a node is transformed into its output, thereby influencing the model's ability to learn and make decisions. Without activation functions, neural networks would be limited to linear mappings, severely constraining their capability to model complex, non-linear relationships.
Key Characteristics of Activation Functions
Non-linearity: The primary reason for using activation functions is to introduce non-linearity into the network. This allows the model to capture complex patterns and interactions in the data that linear models cannot.

Differentiability: For the purposes of backpropagation and gradient-based optimization, activation functions must be differentiable. This means their derivatives should be easily computable.

Range: Different activation functions operate within different ranges. Some are unbounded (e.g., ReLU), while others are bounded (e.g., sigmoid). The range can impact the gradient values during training.

Computational Efficiency: Activation functions need to be computationally efficient, as they are applied to each node in the network across potentially many layers and a large dataset.

[sivananda]

Very clear, crystal explaination. Awaiting for your next concept video 👍