  Tensor networks provide extremely powerful tools for the study of complex
classical and quantum many-body problems. Over the last two decades, the
increment in the number of techniques and applications has been relentless, and
especially the last ten years have seen an explosion of new ideas and results
that may be overwhelming for the newcomer. This short review introduces the
basic ideas, the best established methods and some of the most significant
algorithmic developments that are expanding the boundaries of the tensor
network potential. The goal is to help the reader not only appreciate the many
possibilities offered by tensor networks, but also find their way through
state-of-the-art codes, their applicability and some avenues of ongoing
progress.
