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deep-learning

Optimize neural network development and deployment workflows with PyTorch and TensorFlow.

5|1|Updated Nov 18, 2025
Search Tags:#PyTorch#deep learning#model deployment#TensorFlow#transformers#production ML
One-click install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-data-engineer --skill deep-learning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-learning
Source: https://github.com/pluginagentmarketplace/custom-plugin-data-engineer/tree/main/skills/deep-learning
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-data-engineer --skill deep-learning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yaml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Deep learning workflows are complex to set up, validate, and productionize; this skill provides a structured, opinionated blueprint for building, training, and deploying neural networks with PyTorch and TensorFlow in real-world environments.

Core Features & Use Cases

  • End-to-end deep learning workflows including model design, training loops, evaluation, and deployment.
  • Production-ready patterns: mixed precision, export to TorchScript/ONNX, and scalable inference.
  • Use Case: Build an image classifier, train on a dataset, and deploy the model to an inference service.

Quick Start

Use the deep-learning skill to initialize a minimal training loop for a small image dataset and export the trained model for deployment.

Frequently Asked Questions about deep-learning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I export a trained PyTorch model to ONNX for production inference?▼

Export PyTorch models to ONNX for production inference using structured workflows that enforce end-to-end training loops and support exporting models to TorchScript or ONNX. This enables production-ready inference.

What is the best way to set up an end-to-end deep learning training loop?▼

Set up an end-to-end deep learning training loop using a structured blueprint covering model design, training, evaluation, and deployment. This includes built-in mixed-precision support for optimized neural network development.

Does this deep learning workflow support both PyTorch and TensorFlow?▼

Yes, this deep learning workflow supports both PyTorch and TensorFlow. It provides an opinionated blueprint for building, training, and deploying neural networks with these frameworks in real-world research and production environments.

Can I use mixed precision training to optimize neural network development?▼

Yes, you can use mixed precision training to optimize neural network development. The workflow enforces production-ready patterns including mixed-precision support to accelerate training and improve scalability across environments.

How do I deploy an image classifier to a scalable inference service?▼

To deploy an image classifier to a scalable inference service, build and train the model on a dataset, then export it to TorchScript or ONNX. This creates a production-ready model for real-world inference environments.

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