Stokastik

Machine Learning, AI and Programming

Tag: Conjugate Gradient

Optimization Methods for Deep Learning

In this post I am going to give brief overview of few of the common optimization techniques used in training a neural network from simple classification problems to deep learning. As we know, the critical part of a classification algorithm is to optimize the loss (objective) function in order to learn the correct parameters of the model. The type of the objective function (convex, non-convex, constrained, unconstrained etc.) along with […]

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