Neural-network building blocks · MLD-0186
Computation graph
A graph representing operations and the data dependencies between them.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Track tensor operations for differentiation.
Original caveat
Dynamic and static graphs have different execution tradeoffs.
Context and aliases
Level: Intermediate. Stages: Training · Inference · Theory.
Tags: DL · SLM · LLM · Vision · NLP.
Aliases: Computational graph.
Related concepts
Concepts linking here
Technology connections
- Burn — Rust-native deep-learning framework.
- Equinox — JAX library representing models as PyTrees.
- Flax — Neural-network library built on JAX.
- JAX — Composable automatic differentiation, vectorization and compilation for numerical Python.
- Keras — High-level model-building API supporting multiple numerical backends.
- MLX — Array and ML framework optimized for Apple silicon.
- PyTorch — Tensor computation, automatic differentiation and neural-network framework.
- TensorFlow — Tensor and neural-network framework with deployment tooling.
- tinygrad — Compact tensor and automatic-differentiation framework.
Original references
- Deep Learning: online textbook ↗ · Consulted
- PyTorch neural-network layers ↗ · Reference link
- PyTorch documentation ↗ · Reference link
- Original reference URL ↗