Codeless Deep Learning with KNIME

Chapter 6: Recurrent Neural Networks for Demand Prediction

We have gathered some experience, by now, with fully connected feedforward neural networks in two variants: implementing a classification task by assigning an input sample to a class in a set of predefined classes or trying to reproduce the shape of an input vector via an autoencoder architecture. In both cases, the output response depends only on the values of the current input vector. At time , the output response, , depends on, and only on, the input vector, , at time . The network has ...