Can I use this to modify an existing model?

Absolutely. You can request specific changes to an existing network's layers, hyperparameters, or training process, and Claude will generate the updated configuration.

How do I trigger the Neural Network Builder skill?

You can activate the skill by asking Claude to 'build a neural network,' 'define a CNN architecture,' or by mentioning specific model types like RNN or Transformer.

Does this skill support specific layer types?

Yes, it supports a variety of layers including convolutional, recurrent (LSTM/GRU), transformer, embedding, and fully connected layers.

Does it help with model optimization?

Yes, the skill can suggest and implement regularization techniques like dropout and L1/L2 regularization to help prevent overfitting during training.

Neural Network Builder

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

byintent-solutions-io

데이터 과학 및 ML

Constructs and configures complex neural network architectures using natural language commands within Claude Code.

소개

The Neural Network Builder skill transforms Claude into a specialized deep learning architect capable of designing, modifying, and optimizing sophisticated model architectures. By leveraging the neural-network-builder plugin, it automates the technical heavy lifting of defining layers—such as convolutional, recurrent, or transformer blocks—and configuring training parameters like learning rates and activation functions. Whether you are building an image classifier or a text generation pipeline, this skill ensures your AI models are structured according to best practices while significantly accelerating the development cycle from concept to configuration.

주요 기능

0 GitHub stars
Dynamic modification of existing neural network parameters
Automated architecture design for CNNs, RNNs, and Transformers
Intelligent selection of regularization techniques like dropout
Granular layer configuration including filter sizes and activation functions
Integration with training pipeline command execution

사용 사례

Defining an LSTM-based recurrent architecture for natural language processing
Generating a three-layer convolutional neural network for image classification tasks
Configuring transformer layers and embedding dimensions for custom generative models

소개

주요 기능

0 GitHub stars
Dynamic modification of existing neural network parameters
Automated architecture design for CNNs, RNNs, and Transformers
Intelligent selection of regularization techniques like dropout
Granular layer configuration including filter sizes and activation functions
Integration with training pipeline command execution

사용 사례

Defining an LSTM-based recurrent architecture for natural language processing
Generating a three-layer convolutional neural network for image classification tasks
Configuring transformer layers and embedding dimensions for custom generative models