  Recurrent neural networks are a powerful means in diverse applications. We
show that, together with so-called conceptors, they also allow fast learning,
in contrast to other deep learning methods. In addition, a relatively small
number of examples suffices to train neural networks with high accuracy. We
demonstrate this with two applications, namely speech recognition and detecting
car driving maneuvers. We improve the state-of-the art by application-specific
preparation techniques: For speech recognition, we use mel frequency cepstral
coefficients leading to a compact representation of the frequency spectra, and
detecting car driving maneuvers can be done without the commonly used
polynomial interpolation, as our evaluation suggests.
