Role of Hessian and its Eigen pair in Deep Learning
While we speak of optimization methods in Machine Learning/Deep Learning, stochastic gradient descent and its variants are quite popular. Choosing an optimal learning rate is crucial towards reaching minima. While some are chosen with theoretical reasons, some are empirically driven. Hessian play an important in understanding and driving the learning rates towards achieving convergence at a faster rate. This topic is getting increasingly popular among deep learning researchers with a quest to accelerate training time with effective convergence properties.
In this talk, we will analyze the role of Hessian in driving the error function curvature and also understand the implications of its eigenvalues. Understanding the eigenvalue spectrum can reveal a lot about the behavior of the optimization algorithm.
Outline/Structure of the Talk
The general form of cost function used in traditional machine learning/ deep learning algorithms will be presented. The influence of Hessian ( the second derivative of the cost function) in choosing the learning rate will be demonstrated.
Geometrical understanding of cost functions, learning rate, the importance of convergence will be understood. The highlight of the talk will be that some of the core mathematical concepts will be presented in an elegant manner.
Machine learning/ deep learning practitioners, enthusiasts
Prerequisites for Attendees
Understand the basics of machine learning optimization methods
schedule Submitted 3 years ago
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