The ability to make machines learn is a fascinating achievement of the last decades. Many new business opportunities have opened up, and companies use Machine Learning on a day-to-day basis.
Human language is ambiguous. Speaking (or writing), we convey the individual words, tone, humour, metaphors, and many more linguistic characteristics. For computers, such properties are hard to detect in the first place and even more challenging to understand in the second place.
Recent successes, achieved with the help of Reinforcement Learning, have quite extensively been covered by the media. One can think of DeepMind’s AlphaGo algorithm: It learned to play the age-old game of Go and even developed its own playstyle.
The core idea behind generative networks is capturing the underlying distribution of the data. This distribution can not be observed directly, but has to be approximately inferred from the training data
