JuliaArrays / JuliaArrays/DualArrays.jl
Example: Levenberg-Marquadt Algorithm
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- Dominant language
- Julia
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- 1
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- 1
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Description
Per Fitzgibbon's paper: the optimisations of DualArrays.jl provide a way to speed up the Levenberg-Marquadt algorithm (LM algorithm), particularly useful in fitting small to medium neural networks. This is because the LM algorithm combines the speed of convergence of the Gauss-Newton method with the stability of gradient descent (Yu and Wilamowski, 2010). An example of this on a neural network to showcase this could be good
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the DualArrays.jl package and the cited Fitzgibbon paper to understand how its optimisations relate to the Levenberg-Marquardt algorithm. Identify where an example should live, then add a neural-network fitting example that demonstrates the algorithm and its relevance to small or medium networks.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100