Explore differentiable functional programming in this Scala Days conference talk. Dive into parameterised functions, supervised learning, and gradient descent techniques. Learn about deep learning as supervised learning of parameterised functions, tensor multiplication, and non-linearity. Discover algorithms for calculating gradients, including symbolic differentiation, forward-mode, and reverse-mode approaches. Examine the use of monads and continuations in gradient calculations, and understand how Scala's expressive type system can address tensor dimension challenges. Gain insights into linear logic, differential λ-calculus, and the potential for Scala in developing next-generation deep learning systems.
Overview
Syllabus
Intro
Parametrised functions Supervised learning Gradient descent
Choose parameters to fit data
Algorithmically choose parameters
Calculate the derivative of the loss function with respect to the parameters
Calculate gradient for current parameters
Deep learning is supervised learning of parameterised functions by gradient descent
Tensor multiplication and non-linearity
Tensors: multidimensional arrays
Conditionals and loops
Algorithms for calculating gradients
Composition of Derivatives
Symbolic differentiation
No loops or conditionals
Inexact. Choosing e is hard
Two main algorithms: forward and reverse
Calculate with dual numbers
There is a monad for Dual
Forward-mode scales in the size of the input dimension
Chain rule doesn't care about order
Use a monad (or continuations)
flatMap is the chain rule
Reverse-mode scales in the size of the output dimension
Tensor dimensions must agree
Solution: expressive type systems
Linear logic and differential A-calculus
Need compilation (to GPU) for performance
Scala is well positioned for the next generation of DL systems
Taught by
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