Can this skill help with GPU memory issues?

Yes, it includes specific strategies for resolving CUDA Out-of-Memory (OOM) errors, such as implementing gradient accumulation and reducing batch sizes without losing training signal.

Does it support modern transformer architectures?

Absolutely. It covers multi-head self-attention mechanisms, positional encoding, and building full transformer encoders/decoders from scratch or using pre-trained components.

What deep learning frameworks does this skill support?

The skill provides comprehensive support for PyTorch and TensorFlow, along with ecosystem tools like PyTorch Lightning, Hugging Face Transformers, and ONNX Runtime.

How does it handle model deployment?

It provides patterns for exporting models to production-ready formats like ONNX and TorchScript, and includes configurations for GPU optimization using TensorRT.

Deep Learning Engineering

Name: Deep Learning Engineering
Author: pluginagentmarketplace

bypluginagentmarketplace

•

데이터 과학 및 ML

Builds, optimizes, and deploys production-grade neural networks using PyTorch, TensorFlow, and modern transformer architectures.

The Deep Learning skill empowers Claude to design, implement, and optimize sophisticated neural network architectures for real-world production environments. It provides specialized knowledge in modern frameworks like PyTorch and TensorFlow, covering advanced techniques such as mixed-precision training, gradient accumulation, and transformer implementation. This skill is essential for engineers who need to move beyond research prototypes to build scalable, high-performance models ready for deployment via ONNX, TorchScript, or TensorRT.

주요 기능

012 GitHub stars

02Production training loops with mixed precision and gradient management

03Model optimization and cross-platform deployment via ONNX and TorchScript

04Implementation of modern architectures including CNNs, Transformers, and Residual Blocks

05Integration with experiment tracking tools like Weights & Biases and Ray for distributed training

06Advanced debugging for vanishing gradients, OOM errors, and training bottlenecks

사용 사례

01Optimizing deep learning models for high-throughput, low-latency production inference

02Building custom transformer-based models for specialized NLP or computer vision tasks

03Implementing robust training pipelines with early stopping, learning rate finding, and data augmentation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pluginagentmarketplace/custom-plugin-data-engineer deep-learning

For use in Claude.ai and ChatGPT

Download Skill