Why Combine Momentum and RMSProp?

1. Momentum: Fast convergence by smoothing gradients (accelerates in long-term gradient direction).
2. Adaptive rates (RMSProp): Per-dimension learning rate scaling for stability (handles different feature scales, sparse gradients).
3. Adam uses both: maintains moving averages of both first moment (gradients) and second moment (squared gradients)
4. Additionally, includes a mechanism to correct the bias in these moving averages (crucial in early iterations)

Result: Adam is robust, achieves faster convergence with less tuning, and often outperforms SGD (with momentum) in practice

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