deep-learning
Build and train deep neural networks including CNNs, RNNs, Transformers, and advanced architectures. Use for image classification, object detection, NLP, sequence modeling, transfer learning, and complex pattern recognition tasks.
npx skills add majiayu000/claude-skill-registry --skill deep-learning-jh9282026-manus --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Deep Learning Build and train deep neural networks for computer vision, natural language processing, and complex pattern recognition. ## Overview This skill provides comprehensive guidance for deep learning across convolutional networks, recurrent networks, transformers, and modern architectures. ## Quick Reference | Scenario | Recommended Approach | Reference File | |----------|---------------------|----------------| | Image classification, object detection | CNNs (ResNet, EfficientNet) | `/references/cnn-guide.md` | | Sequential data, time-series, NLP | RNNs, LSTMs, GRUs | `/references/rnn-guide.md` | | Modern NLP, attention-based models | Transformers (BERT, GPT) | `/references/transformer-guide.md` | | Training optimization and techniques | Best practices, regularization | `/references/training-guide.md` | ## Core Principles 1. **Architecture Selection** - Choose network type based on data structure and task 2. **Gradient Flow** - Ensure effective backpropagation through deep networks 3. **Regularization** - Prevent overfitting with dropout, batch norm, data augmentation 4. **Transfer Learning** - Leverage pre-trained models for faster, better results 5. **Optimization** - Us
- Overview
- Quick Reference
- Core Principles
- Network Types
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Transformers
- Training Strategies
- Using the Reference Files
- Best Practices
- Common Pitfalls to Avoid
- Key Frameworks
- Hardware Considerations
What does the deep-learning skill do?
Build and train deep neural networks including CNNs, RNNs, Transformers, and advanced architectures. Use for image classification, object detection, NLP, sequence modeling, transfer learning, and complex pattern recognition tasks.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill deep-learning-jh9282026-manus --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.