Activation functions are essential keys to good performance in a neural network. Many functions can be used, and the choice of which one to use depends on the issues addressed. New adaptable and ...

Neural networks (NN) are architectures and algorithms for machine learning. They are quite powerful for tasks like classification, clustering, and pattern recognition. Large neural networks can be ...

An autoencoder is a type of artificial neural network commonly used to learn efficient representations of data, typically for dimensionality reduction, data compression, or denoising (noise removal).

Neural networks are composed of multiple layers, and the defining aspect of an autoencoder is that the input layers contain exactly as much information as the output layer. The reason that the input ...

This is a simple example of using a neural network as an autoencoder without using any machine learning libraries in Python. The input is a 8-bit binary digits and as expected the output is the same 8 ...

A neural autoencoder is essentially a complex mathematical function that predicts its input. All input must be numeric so categorical data must be encoded. Although not theoretically necessary, for ...