Machine learning is one of the fastest growing Julia Community group. It featured in a workshop session at JuliaCon 2017, part of which was presented by one of this meetup's speakers.
Some ML packages have fallen by the wayside and others are now in the ascendency and in this talk Mike and Avik will discuss what the favoured options are for tackling machine and deep learning problems in Julia and in addition will discuss some of the exciting developments which will be available soon.
Mike Innes and Avik Sengupta are no strangers to the London User Group, each having authored a number of seminal Julia packages. Both now are full time employees of Julia Computing in the UK and are well placed to provide key information as to the way ahead to Julia v1.0
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Machine Learning: The Julian Approach
Mike is a recent physics graduate who managed to combine his undergraduate studies with a number of seminal packages such as Markdown, Lazy, Atom and Blink. He started the Juno project while studying for his degree and now works on it full time for MIT and Julia Computing.