Stokastik

Machine Learning, AI and Programming

Tag: Optimization

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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Convex Optimization in Machine Learning

In an earlier post, we introduced one of the most widely used optimization technique, the gradient descent and its scalable variant, the Stochastic Gradient Descent. Although the SGD is an efficient and scalable technique to optimize a function, but the drawbacks with both gradient descent and SGD is that they are susceptible to find local optimum. The gradient descent technique is not suited to find local or global optimum with […]

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