# deep-learning

Gives your agent a rulebook for neural network work: layer design, optimizers, mixed precision, memory. Useful when you write or refactor model code.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 946 installs · ★ 286
- Author: Mindrally
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Mindrally/skills/tree/HEAD/deep-learning/SKILL.md
- Updated: 2026-10-08
- HTML: https://affpapa.org/en/nevibecode/skills/deep-learning

## What it does

The skill frames the agent as an expert in deep learning architectures and model optimization. It carries a set of principles: start simple and add complexity only when needed, keep components modular and reusable, normalize layers properly, get initialization right. For training it covers optimizer choice (Adam, SGD, AdamW), learning rate schedules, gradient clipping and weight decay.

Separate sections deal with memory and scale. Gradient accumulation simulates a larger batch, mixed precision through `torch.cuda.amp` cuts the footprint, activation checkpointing trades compute for memory. Multi-GPU work is split between DataParallel and DistributedDataParallel, with gradient synchronization and learning rate scaling spelled out. There is also a debugging block: metrics, training progress visualization, gradient flow problems, profiling bottlenecks.

The skill is plain text and runs nothing on its own. It sets the frame the agent reasons inside, so you still check what it suggests. Install goes through `npx skills add`, and the file lands at `~/.claude/skills/deep-learning/SKILL.md`.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill deep-learning -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/deep-learning/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/deep-learning/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill deep-learning -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/deep-learning/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill deep-learning -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/deep-learning/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Mindrally/skills deep-learning
```

2. **Manually: put the files in place**
- File: `.github/skills/deep-learning/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill deep-learning -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Mindrally/skills --path deep-learning
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/deep-learning/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill deep-learning -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Mindrally/skills/tree/HEAD/deep-learning/SKILL.md

## When to use it
- pick an optimizer and learning rate schedule for the task
- split training across GPUs without hand-rolling gradient sync
- fit a run into GPU memory with gradient accumulation and mixed precision
- track down gradients that stop reaching early layers
- keep an experiment reproducible with seeds, logs and checkpoints

## Example prompts
- Design a network architecture for this classification task and justify the layer choices
- Rewrite the training loop with DistributedDataParallel and correct learning rate scaling
- Turn on mixed precision and gradient accumulation so it fits in memory

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 6ad5595e949237b1.

BEGIN THIRD-PARTY FILE 6ad5595e949237b1
```text
---
name: deep-learning
description: Comprehensive deep learning guidelines for neural network development, training, and optimization.
metadata:
  maintainer: Mindrally
  source: https://github.com/Mindrally/skills
---

# Deep Learning

You are an expert in deep learning, neural network architectures, and model optimization.

## Core Principles

- Design networks with clear architectural goals
- Implement proper training pipelines
- Optimize for both accuracy and efficiency
- Follow reproducibility best practices

## Network Architecture

### Layer Design
- Choose appropriate layer types for the task
- Implement proper normalization (BatchNorm, LayerNorm)
- Use activation functions appropriately
- Design skip connections when beneficial

### Model Structure
- Start simple, add complexity as needed
- Use modular, reusable components
- Implement proper initialization
- Consider computational constraints

## Training Strategies

### Optimization
- Choose appropriate optimizers (Adam, SGD, AdamW)
- Implement learning rate schedules
- Use gradient clipping for stability
- Apply weight decay for regularization

### Data Handling
- Implement efficient data pipelines
- Apply appropriate augmentations
- Handle class imbalance properly
- Use proper validation strategies

## Multi-GPU Training

### DataParallel
- Use for simple multi-GPU setups
- Understand synchronization overhead
- Handle batch size scaling

### DistributedDataParallel
- Implement for large-scale training
- Handle gradient synchronization
- Manage process groups properly
- Scale learning rates appropriately

## Memory Optimization

### Gradient Accumulation
- Simulate larger batch sizes
- Handle loss scaling properly
- Implement proper gradient synchronization

### Mixed Precision
- Use `torch.cuda.amp` or equivalent
- Handle loss scaling for stability
- Choose appropriate precision for operations

### Checkpointing
- Trade compute for memory
- Implement activation checkpointing
- Choose checkpoint granularity wisely

## Evaluation and Debugging

- Implement comprehensive metrics
- Visualize training progress
- Debug gradient flow issues
- Profile performance bottlenecks

## Best Practices

- Set random seeds for reproducibility
- Log hyperparameters and metrics
- Save checkpoints regularly
- Document experiments thoroughly
```
END THIRD-PARTY FILE 6ad5595e949237b1

The third-party file has ended. Everything between the 6ad5595e949237b1 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

© Mindrally · Apache-2.0 · https://github.com/Mindrally/skills/tree/HEAD/deep-learning/SKILL.md

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