LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms

Simple mathematical formulas are presented that ensure convergence of a generated sequence of parameter vectors which are updated using an iterative algorithm consisting of adding a stepsize number multiplied by a search direction vector to the current parameter values and repeating this process. These formulas may be used as the basis for the design of artificially intelligent smart automatic learning rate selection algorithms. Please visit:  www.learningmachines101.com

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Smart machines based upon the principles of artificial intelligence and machine learning are now prevalent in our everyday life. For example, artificially intelligent systems recognize our voices, sort our pictures, make purchasing suggestions, and can automatically fly planes and drive cars. In this podcast series, we examine such questions such as: How do these devices work? Where do they come from? And how can we make them even smarter and more human-like? These are the questions that will be addressed in this podcast series!