How does this skill handle model training failures?

It includes built-in retry logic with exponential backoff and a troubleshooting guide to address common issues like exploding gradients or memory errors.

Does it support GPU acceleration?

Yes, it includes patterns for CUDA device management and Automated Mixed Precision (AMP) to significantly speed up training on compatible GPUs.

Can I use this for advanced architectures like Transformers?

Absolutely. The skill covers a range of architectures including MLPs, CNNs, RNNs, and Transformers for various AI tasks.

Which deep learning framework does this skill use?

This skill is specifically optimized for PyTorch, providing guidance on nn.Module, DataLoaders, and the broader PyTorch ecosystem.

Does it provide best practices for model regularization?

Yes, the skill implements and explains key regularization techniques such as Dropout, Weight Decay, and Batch Normalization to prevent overfitting.

Deep Learning with PyTorch

Name: Deep Learning with PyTorch
Author: pluginagentmarketplace

bypluginagentmarketplace

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Ciencia de Datos y ML

Builds, trains, and optimizes neural networks using PyTorch with production-grade patterns and best practices.

Acerca de

The Deep Learning skill provides a comprehensive toolkit for architecting and implementing neural networks ranging from Multi-Layer Perceptrons (MLPs) to Convolutional Neural Networks (CNNs) and Transformers. It streamlines the development process with robust training loop templates, advanced optimization techniques like Mixed Precision (AMP) and OneCycle learning rate scheduling, and integrated observability for monitoring loss and accuracy. Whether you are building image classifiers or sequence models, this skill ensures your PyTorch implementation follows industry standards for performance and maintainability.

Características Principales

Template-based neural network architectures for MLP, CNN, RNN, and Transformers.
Automated model checkpointing and state management for training persistence.
High-performance training loops with Automated Mixed Precision (AMP) support.
1 GitHub stars
Comprehensive troubleshooting guide for common issues like NaN loss and GPU OOM errors.
Advanced optimization strategies including weight decay, dropout, and LR scheduling.

Casos de Uso

Building predictive models for tabular data with optimized Multi-Layer Perceptrons.
Implementing efficient training pipelines that maximize GPU throughput via mixed precision.
Developing and training image classification models using CNN architectures.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pluginagentmarketplace/custom-plugin-machine-learning deep-learning

For use in Claude.ai and ChatGPT

Download Skill

GitHub