Archives for gradient-free optimisation

15 Jul

Top Optimisation Methods In Machine Learning

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image-14023

 “All the impressive achievements of deep learning amount to just curve fitting.”   Judea Pearl Machine learning in its most reduced form is sometimes referred to as glorified curve fitting. In a way, it is true. Machine learning models are typically founded on the principles of convergence; fitting data to the model. Whether this approach will…

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09 Jul

Why Do Neural Networks Generalise So Effortlessly? This Study Might Have An Answer

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image-5677

Deep neural networks are ambiguous for many reasons. They can be as simple as, “How can Stochastic Gradient Descent (SGD) find good solutions to a complicated non-convex optimisation problem?”  However, the answer is not always straightforward. In order to define other such puzzling fundamental questions, the researchers at Facebook AI recently released a new study.…

The post Why Do Neural Networks Generalise So Effortlessly? This Study Might Have An Answer appeared first on Analytics India Magazine.

05 Mar

FB’s New Python Library Nevergrad Provides A Collection Of Algorithms That Don’t Require Gradient Computation

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image-3053

Nevergrad, an open-sourced Python3 toolkit by Facebook for developers offers an extensive collection of algorithms to avoid gradient optimization and present them in a standard ask-and-tell Python framework. The platform enables AI researchers, machine learning scientists, and enthusiasts whose work involves derivative-free optimization to implement state-of-the-art algorithms and methods to compare performance in different settings.…

The post FB’s New Python Library Nevergrad Provides A Collection Of Algorithms That Don’t Require Gradient Computation appeared first on Analytics India Magazine.