Expand description
Ergonomics & safety focused deep learning in Rust. Main features include:
- Tensor library, complete with const generic shapes, activation functions, and more.
- Safe & Easy to use neural network building blocks.
- Standard deep learning optimizers such as Sgd and Adam.
- Reverse mode auto differentiation[1] implementation.
Modules
Collection of traits to describe Nd arrays.
A collection of data utility classes such as one_hot_encode() and SubsetIterator.
Implementations of GradientTape and generic Nd array containers via Gradients.
Standard loss functions such as mse_loss(), cross_entropy_with_logits_loss(), and more.
Contains all public exports.