Activation functions in neural networks aremathematical functions that introduce non-linearity, enabling the
network to learn complex patterns and relationships in data, by transforming the input signal of a node into
an output signal.
Here's a more detailed explanation:
Why are they important?
Non-linearity:
Without activation functions, neural networks would only be able to model linear relationships, limiting their
ability to learn complex patterns.
Learning Complex Patterns:
Activation functions allow neural networks to learn highly complex mappings between inputs and outputs.
Decision Making:
They determine whether a neuron is "activated" or not, based on the weighted sum of its inputs.
Common Activation Functions:
Sigmoid:
S-shaped function that outputs values between 0 and 1, often used in the output layer for binary
classification.
Tanh (Hyperbolic Tangent):
Similar to sigmoid but outputs values between -1 and 1, can be useful in hidden layers.
ReLU (Rectified Linear Unit):
Outputs the input directly if it's positive, otherwise outputs 0, known for its computational efficiency and
mitigation of vanishing gradients.
Softmax:
Outputs a probability distribution over multiple classes, commonly used in the output layer for multi-class
classification.
ELU (Exponential Linear Unit):
An alternative to ReLU, designed to address the "dying ReLU" problem and offer better performance.
Swish:
A relatively new activation function that has shown good performance in some applications.
Leaky ReLU:
A variation of ReLU that allows a small gradient for negative inputs, preventing the "dying ReLU" problem.
Choosing the Right Activation Function:
The choice of activation function depends on the specific task and the architecture of the neural network.
Hidden Layers:
