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Pixel level land classificationTutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.
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Dat7General Assembly's Data Science course in Washington, DC
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Vdom🎄 Virtual DOM for Python
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Hamiltonian NnCode for our paper "Hamiltonian Neural Networks"
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BinpyAn electronic simulation library written in pure Python
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PaddlehelixBio-Computing Platform featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集
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RadioRadIO is a library for data science research of computed tomography imaging
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Text ClassificationText Classification through CNN, RNN & HAN using Keras
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Pydqcpython automatic data quality check toolkit
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NotebookerProductionise your Jupyter Notebooks as easily as you wrote them.
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Atari Model ZooA binary release of trained deep reinforcement learning models trained in the Atari machine learning benchmark, and a software release that enables easy visualization and analysis of models, and comparison across training algorithms.
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Example ScriptsExample Machine Learning Scripts for Numerai's Tournament
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Text detectorText detection model that combines Retinanet with textboxes++ for OCR
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HyperspectralDeep Learning for Land-cover Classification in Hyperspectral Images.
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Analytics ZooDistributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray
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PyhessianPyHessian is a Pytorch library for second-order based analysis and training of Neural Networks
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