A practical example in a hard-to-classify dataset. The need to reduce the complexity of a model can arise from multiple factors, often to reduce the computational requirements.
The need to reduce the complexity of a model can arise from multiple factors, often to reduce the computational requirements. However, complexity can’t be arbitrarily reduced because, after many iterations of training and testing, that’s the one model that provided good results. Research on this topic is active, e.g. [Koning et al., 2019] propose a solution to this same problem for CNNs used for exoplanet detection:
Convolutional Neural Networks (CNNs) suffer from having too many trainable parameters, impacting computational performance … We propose and examine two methods for complexity reduction in AstroNet … The first method makes only a tactical reduction of layers in AstroNet while the second method also modifies the original input data by means of a Gaussian pyramid
The kind of transformation proposed by [Koning et al., 2019], and the one proposed in this article, fit in the second category.
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In this post, let's simplify and explain its working through examples that show how neural networks try to imitate and work similar to the way we think. Explaining the working of a neural network and keeping it as simple as possible by relating human thinking to the working of a neural network.
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History says the 16th century was the time during which the rise of Western civilization occurred. During this time, Spain and Portugal explored the Indian Ocean and opened worldwide oceanic trade routes, and Vasco da Gama was given permission by the Indian Sultans to settle in the wealthy Bengal Sultanate. Large parts of the New World became Spanish and Portuguese colonies