Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
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Low-code framework for building custom LLMs, neural networks, and other AI models
An open source library and framework for deep learning on satellite and aerial imagery.
Official PyTorch implementation of VisionHOPE: Visual Backbones as Self-Modifying Learning Systems.
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
Automated Testing for Set-Top Boxes and Smart TVs