Explaining Adam Optimization with Animations

This video uses animations to provide an in-depth explanation of Adam optimization, an adaptive learning rate algorithm commonly used in deep learning. Adam stands for Adaptive Moment Estimation and is an optimization technique for gradient descent. It is efficient for large datasets and neural networks with many parameters because it requires less memory and computation. Adam works by calculating adaptive learning rates for each parameter from estimates of the first and second moments of the gradients. This makes it more robust to noisy gradient information and allows it to converge faster than standard stochastic gradient descent.