LambdaNet
This artificial neural network library, implemented in Haskell, enables users to create, train, and utilize neural networks through higher-order functions. With a focus on abstraction, it simplifies complex tasks by offering a set of pre-defined functions for various data operations, making it accessible for rapid prototyping while supporting extensibility for advanced users.
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Top LambdaNet Features
- Purely functional architecture
- Haskell implementation
- Higher-order function abstraction
- Composable network functions
- Built-in neuron types
- Fully connected layers
- Random weight initialization
- Customizable training methods
- Quadratic error cost function
- Online and minibatch training
- Model persistence with save/load
- Support for future architectures
- Visualization package for outputs
- Extensible through Haskell knowledge
- Regularization and momentum support
- Scikit-Learn style API
- Sample XOR network example
- Minimal Haskell knowledge required
- Unit testing support
- Nightly build accessibility
