Can I build specific architectures like Transformers or LSTMs?

Yes, the skill is designed to handle various architectures including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) with LSTM/GRU cells, and Transformer models.

Does this skill help with model optimization?

Absolutely. You can use it to modify existing architectures, tune parameters like learning rate and batch size, or implement regularization techniques such as dropout.

How does the Neural Network Builder skill work?

It analyzes your natural language requirements, generates a specific configuration for the neural-network-builder plugin, and executes the build-nn command to construct the model.

What plugin is required for this skill?

This skill utilizes the 'neural-network-builder' plugin to execute commands and manage the underlying model construction logic.

Neural Network Builder

Name: Neural Network Builder
Author: jeremylongshore

byjeremylongshore

•

883

数据科学与机器学习

Automates the design, configuration, and construction of custom neural network architectures like CNNs and Transformers within Claude Code.

关于

The Neural Network Builder skill empowers developers to architect sophisticated machine learning models through intuitive natural language commands. By integrating with the neural-network-builder plugin, it handles the complex task of defining layer hierarchies, activation functions, and training hyperparameters for a wide range of tasks. Whether you are building an image classifier with multiple convolutional layers or a text generation engine using LSTMs, this skill streamlines the development lifecycle from conceptual design to executable model structure, ensuring best practices in regularization and parameter tuning are followed.

主要功能

883 GitHub stars
Support for regularization techniques like dropout and L1/L2
Precision layer and activation function configuration
Automated generation of CNN, RNN, and Transformer architectures
Integration with build-nn command for rapid implementation
Customizable training parameters and hyperparameters

使用场景

Building a CNN for image classification with specific layer requirements
Optimizing existing neural network architectures for better performance
Designing an LSTM-based RNN for sequential data or text generation

关于

主要功能

883 GitHub stars
Support for regularization techniques like dropout and L1/L2
Precision layer and activation function configuration
Automated generation of CNN, RNN, and Transformer architectures
Integration with build-nn command for rapid implementation
Customizable training parameters and hyperparameters

使用场景

Building a CNN for image classification with specific layer requirements
Optimizing existing neural network architectures for better performance
Designing an LSTM-based RNN for sequential data or text generation