ai-agent

hyperspectral-autoencoders: Accelerate AI Insight on Complex Spectral Data

Train unsupervised autoencoders and supervised classifiers on hyperspectral datasets with ready‑to‑use Python tools—ideal for founders building AI‑driven analytics.

From the repo · “Tools for training and using unsupervised autoencoders and supervised deep learning classifiers for hyperspectral data.”

120 stars32 forksPythonGuide quality 8/10 · reviewed Oct 11Updated 10/14/2019100% free · open source
What it does

Hyperspectral-autoencoders is a tool for training and using unsupervised autoencoders and supervised deep learning classifiers to analyze and classify hyperspectral data, allowing startup founders to extract valuable insights from complex spectral datasets.

When to use it
  • •When you need to classify materials or objects based on their spectral signatures
  • •When you want to reduce the dimensionality of large hyperspectral datasets while preserving important features
  • •When you are working with remote sensing or satellite data and need to extract specific information from the spectral bands
Ready-to-paste prompt
python train_autoencoder.py --config config.json --dataset dataset.npy --epochs 100
Heads up: Make sure you have the correct version of tensorflow and numpy installed, as specified in the `requirements.txt` file, to avoid compatibility issues with the autoencoder and classifier models
Saves to your device
Use with Claude
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✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/hyperspectral-autoencoders && curl -fsSL https://workflowstacks.com/api/skills/hyperspectral-autoencoders/claude-skill -o ~/.claude/skills/hyperspectral-autoencoders/SKILL.md
Open in another AI app

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Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

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How hyperspectral-autoencoders: Accelerate AI Insight on Complex Spectral Data works
Codeflow
Free to inspect

Hyperspectral-autoencoders: Accelerate AI Insight on Complex Spectral Data is a medium Python project (~9.5k lines across 31 code files). Setup is light: installs like a normal app. Reading the code is optional. Last commit 86 months ago, MIT license, has a test suite.

Size
Medium codebase
~9.5k lines · 31 code files · ~1 h to skim
Setup
One-command install
Installs like a normal app. Reading the code is optional.
Runs on
Python
No API keys detected
Python 100%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    requirements.txt
    Dependencies and the commands it exposes
  3. 3
    examples/autoencoder_test_CNN_vs_MLP.py
    A worked example — copy this to get going
What's in each folder
docs/Documentation232 files
examples/Examples you can copy16 files
deephyp/Folder5 files
images/Images & static assets4 files
READMEHas testsDocumentedNo CIExamples includedMIT licenseLast update 86 mo ago
Quick Actions
Details
Creator
lloydwindrim
Language
Python
Category
ai-agent
Published
6/2/2019

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