Completed
Scala is well positioned for the next generation of DL systems
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Differentiable Functional Programming
Automatically move to the next video in the Classroom when playback concludes
- 1 Intro
- 2 Parametrised functions Supervised learning Gradient descent
- 3 Choose parameters to fit data
- 4 Algorithmically choose parameters
- 5 Calculate the derivative of the loss function with respect to the parameters
- 6 Calculate gradient for current parameters
- 7 Deep learning is supervised learning of parameterised functions by gradient descent
- 8 Tensor multiplication and non-linearity
- 9 Tensors: multidimensional arrays
- 10 Conditionals and loops
- 11 Algorithms for calculating gradients
- 12 Composition of Derivatives
- 13 Symbolic differentiation
- 14 No loops or conditionals
- 15 Inexact. Choosing e is hard
- 16 Two main algorithms: forward and reverse
- 17 Calculate with dual numbers
- 18 There is a monad for Dual
- 19 Forward-mode scales in the size of the input dimension
- 20 Chain rule doesn't care about order
- 21 Use a monad (or continuations)
- 22 flatMap is the chain rule
- 23 Reverse-mode scales in the size of the output dimension
- 24 Tensor dimensions must agree
- 25 Solution: expressive type systems
- 26 Linear logic and differential A-calculus
- 27 Need compilation (to GPU) for performance
- 28 Scala is well positioned for the next generation of DL systems
