🤔 Activation Functions are an important part of Neural Networks!

🧠 Each neuron in a neural network performs a simple operation on the numbers it receives as input. But for the network to learn, the output must vary slightly with the inputs.

💡 This is where Activation Functions come in! They introduce nonlinearity to allow the network to learn complex patterns.

😀 The most common one is the Rectified Linear Unit (ReLU). It outputs the input directly if it is positive, but outputs 0 if the input is negative: f(x) = max(0,x)

😮 This lets outputs grow as inputs increase, but doesn’t grow without limit like other functions. It adds just the right amount of nonlinearity!

🤓 sigmoid and tanh are also popular. Sigmoid squashes numbers between 0-1: f(x)=1/(1+e-x) Tanh maps to -1 to 1: f(x)=2σ(x)-1

😎 These “squash” outputs to control growth and prevent exploding or vanishing values during training.

🥳 With activation functions introducing nonlinearity, neural networks can learn incredibly complex patterns just like our amazing brains! 🧠