⚡ Activation Functions in Deep Learning

How neurons "fire" and learn complex patterns!

In Deep Learning, activation functions decide how signals flow through a network's neurons. Without them, neural networks would just behave like simple linear models! 🧠💥 Let's explore the most popular activation functions and understand why they matter! 🚀

Function	Formula	Usage
ReLU ⚡	f(x) = max(0, x)	Fast and popular for hidden layers
Sigmoid ➰	f(x) = 1 / (1 + e-x)	Good for binary classification (0 or 1)
Tanh 🔵	f(x) = (ex - e-x) / (ex + e-x)	Better than sigmoid for hidden layers (output range -1 to 1)

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⚡ ReLU (Rectified Linear Unit)

- ReLU sets negative values to 0 and keeps positive values as-is.
- Very simple, yet very powerful!

Pros:
🔹 Fast and efficient
🔹 Reduces chances of vanishing gradients

Cons:
🔸 Dead neurons (ReLU can output 0 permanently for some neurons)

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➰ Sigmoid Function

- Squashes input into (0,1) range.
- Perfect when you need probabilities (like yes/no outputs).

Pros:
🔹 Good for binary outputs

Cons:
🔸 Vanishing gradient problem
🔸 Outputs not centered around zero

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🔵 Tanh Function

- Scales input between -1 and 1.
- Often better than sigmoid for hidden layers.

Pros:
🔹 Centered around zero
🔹 Stronger gradients than sigmoid

Cons:
🔸 Still suffers from vanishing gradients at extremes

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🎯 Quick Challenge!

Which activation function outputs between -1 and 1?

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By Darchums Technologies Inc - April 28, 2025