Why are ConvNets Often Better Than the Rest? Part I

In this series, I will explore convolutional neural networks in comparison to standard neural networks. To begin with, the former is an evolution of the latter. Through analyzing this evolution, it is fascinating to see how particular design differences have such a great impact on performance and overall success. We will highlight these differences in performance and success to illustrate why convnets are often better than standard neural network models. For the context of this article, I will assume you have a basic understanding of the principles related to traditional feedforward and backpropagation processes but want to learn how and why convoluted models are more successful.

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Convolutional nets are famously at the forefront of neural networks and machine learning developments in real-world applications. Due to technological advancements in computing platforms and algorithmic developments, convolutional nets now allow for neural network models to be implemented quickly, effectively, and efficiently on a large scale in business and professional situations. As a result, we are finally seeing image classification and voice recognition that at least matches and sometimes outperforms our own capabilities.

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