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

You can construct a wide variety of architectures, including Convolutional Neural Networks (CNNs) for vision, Recurrent Neural Networks (RNNs) with LSTM cells for sequences, and Transformer models for complex data tasks.

Does this skill handle training parameters?

Yes, the skill can configure specific training parameters such as learning rates, batch sizes, and regularization techniques like dropout or L1/L2 regularization.

How do I trigger the Neural Network Builder?

The skill is triggered by natural language requests involving terms like 'build a neural network,' 'define network architecture,' or specific mentions of network types like 'CNN' or 'Transformer'.

Can I use this skill to modify existing models?

Absolutely. You can request modifications to an existing network's layers, parameters, or training process to optimize its performance.

Does it integrate with other developer tools?

Yes, it utilizes the build-nn command and can be combined with other Claude Code skills for data preprocessing, model evaluation, and FastAPI deployment.

Neural Network Builder

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

byintent-solutions-io

Ciencia de Datos y ML

Automates the design, configuration, and construction of custom neural network architectures like CNNs, RNNs, and Transformers through natural language.

Acerca de

This skill empowers Claude to act as an AI architect, translating natural language requirements into optimized neural network configurations using the neural-network-builder plugin. It streamlines the development of complex models—ranging from image classifiers to text generators—by automatically handling layer definition, activation function selection, and training parameter setup. Whether you are modifying an existing architecture or building a new one from scratch, this skill ensures consistency and follows deep learning best practices to accelerate your machine learning workflow.

Características Principales

Automated architecture generation for CNNs, RNNs, and Transformers
Custom configuration of layers, activation functions, and embeddings
Support for model optimization through parameter tuning and regularization
0 GitHub stars
Seamless integration with the build-nn command-line tool
Natural language to neural network configuration mapping

Casos de Uso

Building image classification models with custom convolutional layer stacks
Modifying existing network architectures to improve performance or prevent overfitting
Designing recurrent neural networks (RNNs) with LSTM cells for NLP tasks

Acerca de

Características Principales

Automated architecture generation for CNNs, RNNs, and Transformers
Custom configuration of layers, activation functions, and embeddings
Support for model optimization through parameter tuning and regularization
0 GitHub stars
Seamless integration with the build-nn command-line tool
Natural language to neural network configuration mapping

Casos de Uso

Building image classification models with custom convolutional layer stacks
Modifying existing network architectures to improve performance or prevent overfitting
Designing recurrent neural networks (RNNs) with LSTM cells for NLP tasks