Understanding the Adam Optimization Algorithm

Here, we will demonstrate a basic MATLAB implementation of the Adam optimization algorithm for minimizing the loss function in Iris dataset

The Adam Algorithm Formulas

The Adam algorithm computes adaptive learning rates for each parameter using the first and second moments of the gradients. Let’s break down the formulas involved in the Adam algorithm:

• Initialize the model parameters (θ), learning rate (α), and hyper-parameters (β1, β2, and ε).
• Compute the gradients (g) of the loss function (L) with respect to the model parameters:
• Update the first moment estimates (m):
• Update the second moment estimates (v):
• Correct the bias in the first (m_hat) and second (v_hat) moment estimates for the current iteration (t)
• Compute the adaptive learning rates (α_t):
• Update the model parameters using the adaptive learning rates:

This is a MATLAB implementation of the Adam optimization algorithm as described above. This implementation can be easily adapted for other loss functions and machine learning models.