What is activation?
An activation is a mathematical function that decides whether a neuron in a neural network should “fire” or stay inactive. It takes the weighted sum of the inputs, adds a bias, and then transforms that value into the output that moves to the next layer.
Let's break it down
- Input values: Numbers coming from the previous layer or raw data.
- Weights: Multipliers that tell the network how important each input is.
- Bias: An extra number added to shift the result.
- Weighted sum: Multiply each input by its weight, add them together, then add the bias.
- Activation function: Applies a rule (like ReLU, sigmoid, or tanh) to the weighted sum, producing the final output for that neuron.
Why does it matter?
Without activation functions, a neural network would be just a series of linear equations, which could only solve simple problems. Activations introduce non‑linearity, allowing the network to model complex patterns such as images, speech, and language.
Where is it used?
Every modern deep‑learning model uses activations:
- Convolutional Neural Networks (CNNs) for image recognition.
- Recurrent Neural Networks (RNNs) and Transformers for text and speech.
- Feed‑forward networks for tabular data.
- Any layer that needs to pass information forward in a network.
Good things about it
- Enables learning of intricate, non‑linear relationships.
- Different functions (ReLU, Leaky ReLU, sigmoid, tanh, softmax) give flexibility for various tasks.
- Simple functions like ReLU are fast to compute, helping speed up training.
- Helps with gradient flow during back‑propagation, especially when chosen wisely.
Not-so-good things
- Some functions (e.g., sigmoid, tanh) can cause vanishing gradients, slowing learning.
- Others (e.g., ReLU) can lead to “dead neurons” that never activate.
- Choosing the wrong activation for a task can hurt accuracy.
- Certain functions add extra computational cost or memory usage, which matters on limited hardware.