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Programmer's approach
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Classroom Contents
Differentiable Functional Programming
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- 1 Intro
- 2 Parametrised functions Supervised learning Gradient descent
- 3 Calculate gradient for current parameters
- 4 Deep learning is supervised learning of parameterised functions by gradient descent
- 5 Tensor multiplication and non-linearity
- 6 Algorithms for calculating gradients
- 7 Composition of Derivatives
- 8 Mathematician's approach
- 9 Symbolic differentiation
- 10 Programmer's approach
- 11 Automatic differentiation approach
- 12 Calculate with dual numbers
- 13 Forward-mode scales in the size of the input dimension
- 14 Chain rule doesn't care about order
- 15 Tensor dimensions must agree
- 16 Solution: expressive type systems
- 17 Need compilation (to GPU) for performance
