A method for training Recurrent Neural Networks (RNNs) that uses a fixed ‘reservoir’ to transform input data and a trainable output layer to interpret it, simplifying the training process and making it effective for tasks requiring memory of past inputs.

• Natural Language Processing
• Time Series Analysis
• Recommendation Systems
• Robotics and Control

Reservoir Computing can be used to train an RNN to perform language modeling tasks, where the reservoir is used to encode the input sequence of words and the output layer is used to predict the next word in the sequence.

• Selecting an appropriate semantic modeling technique