Abstract: Selecting an appropriate step size is critical in Gradient Descent algorithms used to train Neural Networks for Deep Learning tasks. A small value of the step size leads to slow convergence, ...
Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...
DMCN Nash Seeking Based on Distributed Approximate Gradient Descent Optimization Algorithms for MASs
Abstract: A key problem in multiagent multitask systems is optimizing conflict-free strategies, especially when task-assignment is coupled with path-planning. Incomplete information exacerbates this ...
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