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Just for some background, I have been using tensorflow but with the Keras API. But now my job role requires a project where a custom Estimator is needed to be made for a weight based multi class multi label classifier using the dataset API of tensorflow. These are all new topics and although tensorflow docs have information about each topic, I am extremely confused as to what flow to make and which modules to use. It would be of great help if someone could point me towards some material to understand these topics and give some advice towards making a classifier of above mentioned kind. Also a side question: which loss error should I use and how do I incorporate the weights? EDIT: The weights associated with each label (from the multi label dataset) is actually the conversion rate from search page, i.e. (#of orders / # of appearances on the search page)

I do think there is still a very heavy dose of confusion in terms of how people mix automation, machine learning and artificial intelligence.

The task automatic music composition entails generative modeling of music in symbolic formats such as the musical scores. By serializing a score as a sequence of MIDI-like events, recent work has demonstrated that state-of-the-art sequence models with self-attention work nicely for this task, especially for composing music with long-range coherence. In this paper, we show that sequence models can do even better when we improve the way a musical score is converted into events. The new event set, dubbed "REMI" (REvamped MIDI-derived events), provides sequence models a metric context for modeling the rhythmic patterns of music, while allowing for local tempo changes. Moreover, it explicitly sets up a harmonic structure and makes chord progression controllable. It also facilitates coordinating different tracks of a musical piece, such as the piano, bass and drums. With this new approach, we build a Pop Music Transformer that composes Pop piano music with a more plausible rhythmic structure than prior arts do. The code, data and pre-trained model are publicly available.\footnote{\url{https://github.com/YatingMusic/remi}}