Changelog

[Unreleased]

ExCubecl v0.5.0 compatibility: Updated ex_cubecl dependency to >= 0.5.0 with new jason dependency for kernel params JSON encoding
Command struct support: ExCubecl.Command typed struct for pipeline commands (from ex_cubecl v0.5.0)
:u8 dtype support: Picks up new :u8 (8-bit unsigned integer) dtype from ex_cubecl v0.5.0

Updated ex_cubecl minimum version from >= 0.4.0 to >= 0.5.0
Added jason ~> 1.4 dependency (required by ex_cubecl v0.5.0 for kernel parameter encoding)
Fixed async_submit/1 type spec to accept String.t() (matching ex_cubecl's submit/1) instead of ExCubecl.Command.t()
Updated README and guides to reference ex_cubecl v0.5+

Fixed describe/3 compile error in test/cuda_test.exs (reverted to describe/2 with @tag on individual tests)
Model compilation improvements: GPU forward pass via Nx.Defn.jit_apply + ExBurn.Defn.Compiler
Glorot/Xavier initialization: Proper weight initialization for all model parameters
Model summary: Keras/PyTorch-style layer-by-layer summary with ExBurn.Model.summary/1
Layer freeze/unfreeze: freeze/2, unfreeze/2, frozen?/2 for fine-tuning workflows
Device management: to_device/2 for CPU ↔ GPU parameter transfer
Weight decay: L2 regularization support in model compilation and training
Batch shuffling: :shuffle option (default true) in training loop
Nesterov momentum: :nesterov option for SGD optimizer
Gradient accumulation: :accumulate_gradients option for effective larger batch sizes
Accuracy tracking: :accuracy option computes classification accuracy during training
Improved progress reporting: ETA, samples/sec, epoch time in training output
Custom training loops: Public train_step/3 and compute_gradients/3 functions
Improved numerical gradients: :numerical_batch method (~2x faster than :numerical)
Better evaluation: evaluate/2 with accuracy tracking and proper partial batch handling
Training optimization guide: Comprehensive guide covering optimizers, LR schedules, gradient clipping, weight decay, batch size selection, memory optimization, and troubleshooting

Initial Nx.Backend behaviour implementation (basic ops, shape ops, reductions, linear algebra)
Rust NIF bridge to Burn Autodiff via rustler
ExBurn.BurnBridge for direct Burn tensor operations
ExBurn.CubeclBridge for GPU context management
ExBurn.Model for model compilation and management
ExBurn.Training with SGD, Adam, RMSprop optimizers, LR scheduling, gradient clipping, callbacks
ExBurn.Error structured error type
CI pipeline (GitHub Actions) with Elixir tests, Rust fmt/clippy
Guides: Getting Started, Training, Mobile Deployment, Architecture