Activation Function Explorer
The Neuron's Switch: Understanding
How Activation Functions Control AI Output.
Activation Function Explorer
The Linear Problem (The Limitation)
Without an Activation Function, a neural network is just a series of simple multiplications and additions, making it a purely linear model. A linear model can only solve problems that can be drawn with a single straight line (like separating two classes of data). It could never solve complex, real-world tasks like image recognition or language translation.
The tool demonstrates a simple input signal being passed through a purely linear “function” to show its limited range.
Introducing Non-Linearity (The Solution)
The Activation Function is inserted between layers to squash or bend the output of a neuron, introducing non-linearity. This is what gives the neural network its power!
By switching between functions like Sigmoid (squashes output between 0 and 1), Tanh (squashes output between -1 and 1), and ReLU (cuts off negative values), you can see how the output signal is dramatically transformed, allowing the network to model highly complex, curved relationships in the data.
The Dead Neuron (The Danger)
While powerful, Activation Functions have drawbacks. For example, the ReLU function ($f(x) = \max(0, x)$) is fast and effective, but if a neuron receives a large negative input, its output becomes zero and it can stop responding to any input.
This is known as the “Dying ReLU” problem, which effectively halts learning for that part of the network. This tool provides a visual indicator to show when a simulated neuron has entered this “dead” state.
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