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What does deep learning and functional programming have in common? This talk dives into the basic ideas behind deep learning and deep learning frameworks like Tensorflow. You'll discover that deep learning fundamentally builds on composition, one of the central ideas in functional programming. In particular deep learning relies on composition of functions and composition of derivatives.
You will then learn how to calculate derivatives using a family of algorithms known as Automatic Differentiation and how to encapsulate these algorithms in a familiar monadic interface. From this, you will be able to build a toy deep learning system in Scala. Finally, we will look at the future of deep learning frameworks and the rise of 'differentiable programming'.
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Noel is a founder of ScalaBridge London, and a Scala consultant at Inner Product and Underscore. In his day job he helps companies large and small achieve more with Scala. Outside of work he’s interested in generative art and machine learning.