# Rust ML WG Meeting 00009

[](https://hackmd.io/MojInFanSQSlDQR2QKKR7g)

## Meeting Info

Date: 20200909

Start time: 1600 ET

Zoom link: https://mozilla.zoom.us/j/99658259391
- Meeting ID: 996 5825 9391

## Agenda

- Rust-RL: Short presentation

- Collect Information about active and advanced ML respositories not represented here on Zulip.

- Linfa
- New tests for `trees` in the works
- linfa-ensemble in the works (starting from random-forest)
- Documentation is currently being updated

- There's currently a 'Book' repository, which is private to the Rust-ML group.
- Documentation for linfa should be probably take priority, but it would be great if these two could influence each other
- This seems like a good 'first issue' for people looking to get involved with the group or machine learning in general. Examples with real-life data sets being imported, applied, etc. could be used here
- This is currently being done in `mdbook` format--considering the popularity of Jupyter in the machine learning domain and the [evcxr](https://github.com/google/evcxr) kernel, should an alternate format be considered?

- The Rust London User Group ML/DS meetup

## Participants

- Ricky (degausser)
- chrism (quietlychris)
- manuel (ZuseZ4)
- Daniel (xd009642)
- Lorenz (bytesnake)
- Samuel Lim

## Minutes

### Rust RL

- https://github.com/ZuseZ4/Rust_RL
- part of a university project
- half neural network, half reinforcement learning
- softmax
- using ndarray
- next steps, optimization adam
- MNIST 98.x% accuracy
- QLearning and DeepQ learning
- able to learn basic games
- would appreciate a review, but is still a heavy WIP
- Q: how much work would it be to work on Pac-Man?
- depends if there's a working implimentation
- his NN can take a 1D, 2D, or 3D
- might be able to run it in a Android emulator and output images
- would first have to implemtent GPU support to work
- Q: storing weights?
- not yet
- chrism has been looking into this as well
- found since most things are dynamic serde doesn't like Strings
- Darknet
- config file
- information about the network and its parameters
- weights file
- Onyx is a generalized weight file
- https://github.com/onnx/onnx
- https://onnx.ai/
- https://crates.io/crates/tract-onnx

- https://www.khronos.org/blog/nnef-and-onnx-similarities-and-differences#:~:text=On%20the%20technical%20side%2C%20NNEF,graph%20as%20a%20data%20structure
- there's also Pickel and H5
- plenty of generalized things to look into and support

### Repos not Represented

- Reinforcement Learning Crates
- some kinda big projects not represented on Zulip channel
- are they are on arewelearningyet.com?
- maybe one or two crates
- should we have a deeper look at crate that might exist
- https://github.com/spearow/juice
- main contributor is on Zulip
- https://github.com/finalfusion
- Not reasonable for people to know about us yet
- should make sure the projects are captured on arewelearningyet.com as soon as we know about them though

### Linfa

- new tests for the `trees` module
- random forest
- chrism has a PR for some new documentation under review
- There's a Julia ML library that has some good documentation--worth taking a look at
- Also this: http://dlib.net/ml_guide.svg
- And this one: https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
- lorenz did review for ensamble learning today
- looking good
- Merging the PR for the `0.2` documentation
- CI was failing, because of `cargo fmt` issues
- doesn't have a README in its sub crate
- https://github.com/rust-ml/linfa/tree/master/linfa-reduction
- https://github.com/rust-ml/linfa/tree/master/linfa-svm
- https://github.com/rust-ml/linfa/tree/master/linfa-trees
- https://github.com/rust-ml/linfa/tree/master/linfa-kernel

### Book

- potential showcase for machine learning in rust
- would be nice to have a "cookbook" like section
- how to use basic datasets (or how we are loading them etc)
- reference equasions and different algorithms
- Definitely should have some support for [plotly](https://igiagkiozis.github.io/plotly/content/getting_started.html) charts for our outputs
- might be some great getting started issue
- should probably have a basic chapter or guides before we ask them to start it up
- luca's ndarray guide
- https://www.lpalmieri.com/posts/2019-12-01-taking-ml-to-production-with-rust-a-25x-speedup/
- https://www.lpalmieri.com/posts/2019-04-07-scientific-computing-a-rust-adventure-part-2-array1/
- we should just open up the book for contributions
- done :white_check_mark:

### Rust London Meetup

- No firm date but end of November (after the 20th)
- 0x009642 presenting
- could do a linfa example
- annouce the `0.2` release, or aiming for the release to coinside with it
- compare and contrast with SkLearn (Scikit-learn)

## Actions
- check on crates that might exist but are not tracked on arewelearningyet.com
- Open up `book` repository to public
- Make sure CI passes on linfa docs update, then push
- chrism
- add `README.md` for all subcrates of `linfa`, describing at least the purpose of the algorithm and how to run the example