Compose :: Melbourne will feature many excellent speakers. One of this year's lineup is Huw Campbell. If you want to see the whole lineup look here!
Deep learning and deep neural networks are currently fields of great interest and excitement, due to their impressive results in supervised and unsupervised machine learning tasks across a broad range of fields including computer vision, speech recognition, and games such as Go.
In this talk, I'll talk about my recent explorations in using type level and purely functional programming techniques applied to deep learning and neural networks. We'll run over deep neural networks theory, their common architectures, and their various use cases to obtain an appreciation for the depth of the field. I'll then show how the use of modern Haskell programming allows for these networks to be expressed at the type level, providing not only extreme concision, but also compile time safety, functional reasoning and composition, as well as pretty good runtime performance with a high degree of confidence in their correctness.
Huw Campbell is a physicist, data scientist, and functional programmer, currently working at Ambiata in Sydney. Coming from an academic background with a PhD in experimental physics, Huw started working as an applied researcher in machine learning in 2014. With Ambiata's broad Haskell experience and mentorship, and a strong interest in functional programming and type systems, Huw decided to apply these principles to machine learning - bringing together these diverse fields.