We leverage a unique patented algorithm to adjust the coefficients within a model. Unlike most algorithms Neuton’s algorithm is not based on backward propagation of errors and the stochastic gradient descent. Furthermore, the Neuton algorithm minimizes the likelihood of hitting local minima. This is especially important when building small models where the probability of hitting local minima is particularly high. Our algorithmic approach helps to avoid this issue.

The Neuton platform enables models to be built automatically, neuron by neuron, starting from learning general features and moving toward identifying the most specific ones. This allows selection of a model of almost any level of precision and size, in a single iteration.

Neuton eliminates the time-consuming multidimensional manual search for neural network parameters (number of layers, neurons in a layer, type of activation function, batch size, learning rate, etc.) and allows one to quickly & efficiently find the optimal structure.

The step-by-step growth of the neural network makes it possible to cross-validate with the addition of each neuron, which is typically not feasible with a standard approach. Furthermore, constant cross-validation increases the generalizing capabilities of the model, which allows for creation of compact models - without compromising accuracy.