Which activation works best for my use case (ReLU vs. sigmoid vs. tanh)?

How It Works:

Benchmark different functions: ReLU for deep, sparse gradients; sigmoid/tanh for shallow nets or where output range matters; softmax for multi-class probabilities.

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Key Benefits:

• Optimized performance: Tailored speed and accuracy trade-offs.
• Stable training: Minimizes vanishing or exploding gradients.
• Predictable behavior: Known convergence properties.

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Real-World Use Cases:

• NLP models: Tanh in small RNNs for sentiment analysis.
• Time series: Leaky ReLU in forecasting networks to handle negative values.