This is a learning project as part of my quest to find deep learning tools that don't infuriate me.
Tensor is a recursively-defined data type which holds a scalar value or a fixed-length vector of Tensors. Scalar, Vector, and Matrix are type aliases for Tensor with different ranks.
This module defines various mathematical functions, including tensor operations.
Layer is a data type which can represent various sorts of neural network layers. This module also defines how to perform a forward pass for each sort of layer.
Network is a data type composed of one or more layers. This module also defines how to perform a forward pass through the entire network, as well as how to calculate a loss value given some data and a loss function.
Variable represents a value which can form a computational graph, store information about gradients, and backpropagate those gradients. It is an instance of Num, Fractional etc so any computations consisting of mathematical functions defined for those interfaces (as well as exp, log, and pow) can be backpropagated via automatic differentation.
This module defines the training loop for supervised learning using backpropagation to successively adjust the parameters of a neural network.