NN Maker

NN Maker provides a domain-focused environment for rapid neural network prototyping. It targets data scientists and ML engineers who need to iterate on architectures without full training cycles. The tool models networks as layer stacks with explicit sizes, activations, and simple training simulations to inform decisions.
The system validates inputs, computes parameter counts, and outputs an architecture specification suitable for export. It supports common templates (MLP, lightweight CNNs, and simple RNN variants) and allows optional features such as dropout and regularization to explore generalization effects.
Conceptually, NN Maker treats a model as a graph of layers; users define input/output dimensions, then enumerate hidden layers. The result is a structured design that can be reviewed, adjusted, and exported to standard formats for later integration.
Unique value lies in lightweight, planning-focused simulations that estimate training steps and resource needs, plus artifact export for onboarding into pipelines. It is particularly useful for baseline design, pre-flight checks before data collection, and fast iteration cycles in research and product teams.