How does Claude translate my requests into a model?

Claude analyzes your performance goals and task description to generate a configuration for the build-nn command, specifying layers, filters, and activation functions automatically.

Can I use this skill to modify existing models?

Yes, the skill is designed to handle requests for modifying existing layers, parameters, or training processes in addition to creating new architectures.

What types of neural networks can I build with this skill?

You can design a wide variety of architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) with LSTM cells, and Transformer models.

Does this require knowledge of specific ML frameworks?

While knowledge of ML concepts helps, the skill simplifies the process by handling the technical syntax of the neural-network-builder plugin based on your high-level requirements.

Neural Network Builder

Name: Neural Network Builder
Author: intent-solutions-io

byintent-solutions-io

Ciencia de Datos y ML

Automates the design and configuration of deep learning architectures including CNNs, RNNs, and Transformers through natural language commands.

Acerca de

This skill empowers developers to architect complex neural networks by leveraging the neural-network-builder plugin within Claude Code. It translates natural language requirements into precise model configurations, allowing users to define layers, activation functions, and training parameters without manual boilerplate. Whether building an image classifier or a text generation engine, this skill streamlines the transition from conceptual model design to executable code, ensuring best practices in architecture and regularization are followed.

Características Principales

Natural language translation to build-nn plugin commands
Automated configuration for CNN, RNN, and Transformer architectures
Customizable layer parameters and activation function selection
Support for regularization techniques like Dropout and L1/L2
0 GitHub stars
Integration with Nixtla-based TimeGPT and FastAPI service pipelines

Casos de Uso

Constructing LSTM-based recurrent architectures for sequence modeling and text generation
Designing multi-layer convolutional networks for specialized image classification tasks
Prototyping transformer models for advanced NLP or time-series forecasting pipelines

Acerca de

Características Principales

Natural language translation to build-nn plugin commands
Automated configuration for CNN, RNN, and Transformer architectures
Customizable layer parameters and activation function selection
Support for regularization techniques like Dropout and L1/L2
0 GitHub stars
Integration with Nixtla-based TimeGPT and FastAPI service pipelines

Casos de Uso

Constructing LSTM-based recurrent architectures for sequence modeling and text generation
Designing multi-layer convolutional networks for specialized image classification tasks
Prototyping transformer models for advanced NLP or time-series forecasting pipelines