Month: October 2023

Abstract: In training an artificial neural network, we need to find model parameters that optimize the cost function while ensuring sufficient learning rate of the machine. Gradient descent and in particular the stochastic gradient descent, comes in handy as an iterative optimization approach to address this need. In this talk, we shall present how this can be achieved i.e. how the cost function…

Abstract: Artificial neural networks are designed to mimic the way neurons in the brain interact with each other in an attempt to achieve a level of thinking similar to what human beings can attain.  This talk looks at the beginnings of implementation of neural networks, the limitations and challenges neural networks have faced, and adjustments…