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

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

Do I need to write the network configuration manually?

No, Claude analyzes your natural language requirements and automatically generates the appropriate configuration for the neural-network-builder plugin.

Can I modify existing neural network models?

Yes, this skill allows you to modify the layers, parameters, or training processes of existing architectures to optimize performance.

Which command does this skill use to create the model?

The skill utilizes the 'build-nn' command provided by the neural-network-builder plugin to construct the network based on the generated configuration.

Neural Network Builder

Name: Neural Network Builder
Author: BbgnsurfTech

byBbgnsurfTech

•

Data Science & ML

Constructs and configures custom neural network architectures including CNNs, RNNs, and Transformers directly within the Claude Code environment.

About

This skill empowers developers to architect, modify, and implement complex neural network models by leveraging a specialized plugin to automate layer configuration and parameter setting. It streamlines the machine learning workflow by translating high-level architectural requirements—such as layer types, activation functions, and training parameters—into executable configurations, making it ideal for tasks ranging from image classification to natural language processing.

Key Features

Automated architecture generation for CNNs, RNNs, and Transformers
Dynamic modification of existing network parameters and training processes
Command-line execution via the specialized build-nn tool
Seamless layer configuration and activation function selection
Integrated support for advanced cell types like LSTM and embedding layers
3 GitHub stars

Use Cases

Designing a multi-layer convolutional neural network for high-accuracy image classification tasks
Building recurrent neural networks with LSTM cells for text generation and language modeling
Optimizing model performance by fine-tuning layer parameters and implementing regularization

About

Key Features

Automated architecture generation for CNNs, RNNs, and Transformers
Dynamic modification of existing network parameters and training processes
Command-line execution via the specialized build-nn tool
Seamless layer configuration and activation function selection
Integrated support for advanced cell types like LSTM and embedding layers
3 GitHub stars

Use Cases

Designing a multi-layer convolutional neural network for high-accuracy image classification tasks
Building recurrent neural networks with LSTM cells for text generation and language modeling
Optimizing model performance by fine-tuning layer parameters and implementing regularization