Your Guide to Autoencoders
<p>Our minds extract and compress knowledge from the world, which we reuse to face other similar situations. One of the critical aspects of that process is that we don’t store all the details of the actual event: just the essential information that allows us to recreate it.</p>
<p>What if you could use Machine Learning to do the same thing? Could boil down knowledge into a reduced data space to be used later? That is what Autoencoders do.</p>
<p>An autoencoder is an Artificial Neural Network algorithm capable of discovering structure within data to develop a compressed representation of some input. It does this, in simple terms, by learning to copy its input to its output.</p>
<p>Autoencoders were designed to encode a data input into a compressed and meaningful representation and then decode it back such that the reconstructed output is as similar as possible to the original input. An autoencoder aims to learn a lower-dimensional representation of higher-dimensional data while maintaining the most crucial information from the initial input.</p>
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