Create a working prototype of a neural network using schotastic gradient descent. Designed for the networks to be easily customisable and exportable.
Includes custom, light-weight linear algebra library.
mkdir build
cd build
cmake ..
make
./main_app
- add documentation
- add test suite / more examples of simple tests
- optimise efficiency (remove obsolete variables)
- separate into mini batches
- more descriptive error messages to stderr
- remove unecessary imports in certain fiels
- make class variables private as necessary (and convert to setters/getters)
- refactor importing neural network (currently includes repeated code)
- more comprehensive documentation
- make (input, target) a tuple / std::pair
- use strategy / builder / template pattern to allow for different activation functions, cost functions, etc.
- create a data loader class / handles all the feeding data into network and getting predictions
- linear_algebra
- make it easier to initalise vectors and matrices...
- use templates (T extends number)