10 Tips to Boost Performance of your AI Models
These model enhancements techniques apply to deep neural networks (DNNs) used in AI. The focus is on the core engine that powers all DNNs: gradient descent, layering and loss function. Reparameterization — Typically, in DNNs, many different parameter sets lead to the same optimum: loss minimization. DNN models are non-identifiable. This redundancy is a strength that […]
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A New Type of Non-Standard High Performance DNN with Remarkable Stability
I explore deep neural networks (DNNs) starting from the foundations, introducing a new type of architecture, as much different from machine learning than it is from traditional AI. The original adaptive loss function introduced here for the first time, leads to spectacular performance improvements via a mechanism called equalization. To accurately approximate any response, rather […]
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