Lecture
This lecture covers the concepts of RMSprop and ADAM, two popular methods for optimizing minibatch gradient descent in Artificial Neural Networks. The instructor discusses the error function, minima, saddle points, momentum, and the implementation details of RMSprop and ADAM algorithms. The lecture also explores the signal-to-noise ratio in stochastic gradient evaluation and the core ideas behind RMSprop with Nesterov Momentum. Additionally, the Adam algorithm is explained in detail, including the step size, moment estimates, and numerical stabilization. The lecture concludes with a summary highlighting the benefits of momentum in optimization algorithms and the adaptability of Adam and its variants.