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name: neural-network-components description: "Routes numpy-ml neural-network component, loss, optimizer, scheduler, and toy-model tasks with CPU-only construction guidance." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Neural Network Components

Use this sub-skill for numpy-ml tasks that work with NumPy-based neural network building blocks rather than training a large end-to-end deep-learning system:

• activations;
• layers and modules;
• losses;
• optimizers and schedulers;
• initializers and wrappers;
• small toy models such as VAE, WGAN-GP, and Word2Vec.

First Checks

1. Read references/api-reference.md for the constructor signatures and the usual forward / backward / update conventions.

2. Run the smoke helper for a tiny CPU-only sanity check:

   python sub-skills/neural-network-components/scripts/neural_component_smoke.py
   

3. Read references/workflows.md when you need a tiny composition example or a safe pattern for a batch of layers.

4. Read references/troubleshooting.md for shape, cache, optimizer, and compatibility failures.

Route by Task

User asks for	Use this route
activation or loss class names and defaults	references/api-reference.md and the smoke helper.
dense, convolutional, recurrent, attention, embedding, or wrapper layers	layer/module sections of api-reference.md.
optimizer or learning-rate scheduler choice	optimizer/scheduler sections.
VAE/WGAN-GP/Word2Vec construction	toy-model notes in workflows.md and api-reference.md.
feature prep, tokenization, or tabular arrays	route to ../preprocessing-and-utilities/SKILL.md first.

Operating Notes

• This package is a NumPy implementation library; do not imply autograd, PyTorch, TensorFlow, or GPU execution as runtime requirements.
• Most methods mutate layer state in place. Keep the object and inspect its parameters/derived variables after the call.
• The repo's comparison tests use external frameworks as baselines, but those frameworks are optional diagnostics, not the runtime dependency of this sub-skill.