Hyperspectral Autoencoders for Insights
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 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
python train_autoencoder.py --config config.json --dataset dataset.npy --epochs 100
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/hyperspectral-autoencoders && curl -fsSL https://workflowstacks.com/api/skills/hyperspectral-autoencoders/claude-skill -o ~/.claude/skills/hyperspectral-autoencoders/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/hyperspectral-autoencoders && curl -fsSL https://workflowstacks.com/api/skills/hyperspectral-autoencoders/claude-skill -o ~/.claude/skills/hyperspectral-autoencoders/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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