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Training neural networks v1.0.0

Use this resource to design and train neural networks reproducibly

New

  • 500 downloads
  • 143 likes
  • 133 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install deep-learning-neural-networks

With no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.

First time: connect the CLI with

npx --yes palatekit@latest login

The agent designs neural networks: layer choice, normalization, residual connections, training strategy and optimization. It also covers DataParallel, DistributedDataParallel and memory reduction. You get a reproducible training pipeline.

Mostly picked by

Updated November 2, 2026

Card for the “Training neural networks” skill

Details

Generates with
Your agent's model
Licence
Apache-2.0

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What you get

  • SKILL.md: layer architecture, model structure and training
  • SKILL.md: data loading, multi-GPU training and memory

Good fit for

  • Your training run fills the GPU memory
  • You build a first network to classify images

Not for

    Tags

    • deep learning
    • neural networks
    • gpu

    About

    You say

    “Design a small convolutional network to classify images, with reproducible training.”

    Card for the “Training neural networks” skill

    Skill published by mindrally under the Apache-2.0 license, delivered in its original version.

    Show more

    How to use it

    1. Install the skill in your agent's skills folder
    2. Ask your agent to design and train neural networks reproducibly
    3. Review and adjust the result

    Content preview

    Files

    • LICENSE11.3 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • SKILL.md2.4 KB

    Origin and licence

    Listed unmodified

    Author
    mindrally
    Source
    github.com/mindrally/skills/tree/7682ca77710e0971eab4e0ae5dddfa281aea0ba5/deep-learning
    Licence
    Apache-2.0

    Notice

    « deep-learning » (mindrally, Apache-2.0) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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