LambdaNet

Networks are built by composing small, reusable functions, enabling rapid prototyping and clear mathematical modeling, as emphasized in the library's philosophy.

Supports online, minibatch, and fixed-iteration training with backpropagation, providing options for different dataset sizes and training scenarios.

Adopts a Scikit-Learn inspired interface with `fit` and `predict` functions, reducing the learning curve for those experienced with machine learning libraries.

Users can easily write custom components like new neuron types or cost functions, fostering experimentation and library growth.