1. Overview
  2. Why Burn?
  3. Getting started
    1. Examples
  5. Basic Workflow: From Training to Inference
    1. Model
    2. Data
    3. Training
    4. Backend
    5. Inference
  7. Building Blocks
    1. Backend
    2. Tensor
    3. Autodiff
    4. Module
    5. Learner
    6. Metric
    7. Config
    8. Record
    9. Dataset
  9. Performance
    1. Good practices
      1. Asynchronous Execution
      2. Kernel Fusion
      3. Kernel Selection
    3. Quantization
    4. Distributed Computing
  11. Custom Training Loop
  12. Saving & Loading Models
  13. Importing Models
    1. ONNX Model
    2. PyTorch Model
    3. Safetensors Model
  15. Models & Pre-Trained Weights
  16. Advanced
    1. Backend Extension
      1. Custom CubeCL Kernel
      2. Custom WGPU Kernel
    3. Custom Optimizer
    4. WebAssembly
    5. No-Std