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-# ActivationFWorks
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+# ActivationFWorks
+
+This repository is a compact reference for common **neural-network activation functions** and how they are typically used across different model families.
+
+## Activation functions
+
+### 1) Sigmoid
+- Formula: `σ(x) = 1 / (1 + e^-x)`
+- Output range: `(0, 1)`
+- Common use: binary output probabilities.
+
+### 2) Tanh
+- Formula: `tanh(x) = (e^x - e^-x) / (e^x + e^-x)`
+- Output range: `(-1, 1)`
+- Common use: hidden layers when centered outputs are useful.
+
+### 3) ReLU
+- Formula: `ReLU(x) = max(0, x)`
+- Output range: `[0, ∞)`
+- Common use: default hidden-layer activation in many deep networks.
+
+### 4) Leaky ReLU
+- Formula: `LeakyReLU(x) = max(ax, x)` where `a` is small (e.g., `0.01`)
+- Output range: `(-∞, ∞)`
+- Common use: mitigates “dying ReLU” behavior.
+
+### 5) ELU
+- Formula: `ELU(x) = x` if `x > 0`, otherwise `a(e^x - 1)`
+- Output range: `(-a, ∞)`
+- Common use: smoother negative branch than ReLU family.
+
+### 6) Softmax
+- Formula: `softmax(x_i) = e^{x_i} / Σ_j e^{x_j}`
+- Output range: `(0, 1)` and sums to `1` across classes.
+- Common use: multi-class output layers.
+
+## Typical model + activation pairings
+
+- **MLP / Feed-forward networks**: ReLU/Leaky ReLU in hidden layers, Sigmoid or Softmax in output.
+- **CNNs**: ReLU-family activations in convolution blocks, Softmax/Sigmoid at output depending on task.
+- **RNN/LSTM/GRU**: Tanh and Sigmoid gates are standard; task-dependent output activations.
+- **Transformers**: ReLU-family alternatives (often GELU in practice) in feed-forward blocks, Softmax for attention/output distributions.
+
+## Quick selection guide
+
+- Use **ReLU** as a strong baseline for hidden layers.
+- Use **Sigmoid** for binary classification output.
+- Use **Softmax** for single-label multi-class output.
+- Consider **Leaky ReLU/ELU** if ReLU units stop activating.
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