Neural networks

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Neural networks were inspired by the architecture of the biological visual cortex. Deep learning is a set of techniques for learning in neural networks that involves a large number of “hidden” layers to identify features. Hidden layers come between the input and output layers. Each layer is made up of artificial neurons, often with sigmoid or ReLU (Rectified Linear Unit) activation functions.

In a feed-forward network, the neurons are organized into distinct layers: one input layer, any number of hidden processing layers, and one output layer, and the outputs from each layer go only to the next layer. In a feed-forward network with shortcut connections, some connections can jump over one or more intermediate layers. In recurrent neural networks, neurons can influence themselves, either directly, or indirectly through the next layer.

Supervised learning of a neural network is done just like any other machine learning: You present the network with groups of training data, compare the network output with the desired output, generate an error vector, and apply corrections to the network based on the error vector, usually using a backpropagation algorithm. Batches of training data that are run together before applying corrections are called epochs. As with all machine learning, you need to check the predictions of the neural network against a separate test data set. Without doing that you risk creating neural networks that only memorize their inputs instead of learning to be generalized predictors.