  The current wave of deep learning (the hyper-vitamined return of artificial
neural networks) applies not only to traditional statistical machine learning
tasks: prediction and classification (e.g., for weather prediction and pattern
recognition), but has already conquered other areas, such as translation. A
growing area of application is the generation of creative content, notably the
case of music, the topic of this paper. The motivation is in using the capacity
of modern deep learning techniques to automatically learn musical styles from
arbitrary musical corpora and then to generate musical samples from the
estimated distribution, with some degree of control over the generation. This
paper provides a tutorial on music generation based on deep learning
techniques. After a short introduction to the topic illustrated by a recent
exemple, the paper analyzes some early works from the late 1980s using
artificial neural networks for music generation and how their pioneering
contributions have prefigured current techniques. Then, we introduce some
conceptual framework to analyze the various concepts and dimensions involved.
Various examples of recent systems are introduced and analyzed to illustrate
the variety of concerns and of techniques.
