6.b) Explain the Adam algorithm in detail.
Answer:
The Adam (Adaptive Moment Estimation) algorithm is an advanced optimization algorithm widely used for training deep learning models. It combines ideas from both momentum and RMSProp, making it effective for noisy gradients and sparse datasets.
- Adam is yet another adaptive learning rate optimization algorithm and is presented in algorithm 8.7.
- The name “Adam” derives from the phrase “adaptive moments.”
- In the context of the earlier algorithms, it is perhaps best seen as a variant on the combination of RMSProp and momentum with a few important distinctions.
- First, in Adam, momentum is incorporated directly as an estimate of the first order moment(with exponential weighting) of the gradient.
- The most straightforward way to add momentum to RMSProp is to apply momentum to the rescaled gradients.
- Second, Adam includes bias corrections to the estimates of both the first-order moments (the momentum term) and the (uncentered) second-order moments to account for their initialization at the origin (see algorithm 8.7).
- Adam is generally regarded as being fairly robust to the choice of hyperparameters, though the learning rate sometimes needs to be changed from the suggested default
Algorithm
