machine-learning
Projects with this topic
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Project Repository for the MLDS Course
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Auctions & bids online live system
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A VERY early alpha, don't use. Or use, but refactor first and send a PR.
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Bike Sharing in Washington D.C.
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Loading a trained neural network and running predictions on locally saved images. Topics: #algorithm #image-2d #machine-learning #sample #sick-appspace
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Training a set of cereal types and classifying a test set. Topics: #algorithm #image-2d #machine-learning #sample #sick-appspace
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Provide a good solution to the CFM 2020 Challenge the hard way (hand writing the optimizer, directly use the Linear Algebra routines, use the most out of the multicore hardware).
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A replacement for patsy better suitable for fully automated use
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Introduction to classification using machine learning and deep learning (PyTorch, TensorFlow, Keras)
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Neural network for object classification
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Training the system with binary classification to classify cookies as correctly oriented or flipped Topics: #algorithm #image-2d #machine-learning #sample #sick-appspace
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Fast Flexible Replay Buffer Library
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A Crystal library for scientific computing with a focus on data science and machine learning.
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Data Science / Machine Learning Pipeline component for training and deploying ML models using CI
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Example showing the use of a pretrained classification network. See also the available tutorials on the SICK support portal. Topics: #algorithm #image-2d #machine-learning #deep-learning #neural-network #sample #sick-appspace
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Example showing the use of a pretrained classification network together with an EdgeMatcher to inspect multiple image regions. See also the available tutorials on the SICK support portal. Topics: #algorithm #image-2d #machine-learning #deep-learning #neural-network #sample #sick-appspace #edgematcher #locator #matching
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An implementation of the generalised Discriminative Restricted Boltzmann Machine
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An Active Learning framework for Drug Discovery with the ability to create targeted models for particular experiments
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This project aims to develop a robust and interpretable machine learning model for early detection of cyberattacks within network systems. By analyzing network traffic data and identifying patterns associated with malicious activities, the model strives to improve network security and prevent potential cyber threats.
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