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Getting-Things-Done-with-Pytorch
curiousily/Getting-Things-Done-with-Pytorch
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER
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Jupyter Notebook
Apache-2.0
Updated: 1w ago
📋 Project at a Glance
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What's this?A , built with Jupyter Notebook open-source project in the Learning & Resources category, core strengths: anomaly-detection/bert
Who made it?Maintained by curiousily team, 2.5K⭐ on GitHub, #778 out of 3640 in Learning & Resources
Why does it exist?The curiousily team recognized that existing anomaly-detection tools in Learning & Resources were hard to use. Getting-Things-Done-with-Pytorch was designed to make bert more accessible.
What can it do?Key use cases: computer-vision, coronavirus, deep-learning
How to install with AI?Use an AI coding assistant to follow the README and automatically handle the install and environment setup.
🔗 github.com/curiousily/Getting-Things-Done-with-Pytorch | 官网 https://mlexpert.io
🔗 github.com/curiousily/Getting-Things-Done-with-Pytorch | 官网 https://mlexpert.io
Topics
anomaly-detectionbertcomputer-visioncoronavirusdeep-learningface-detectionface-recognitionlstmmachine-learningnlpobject-detectionpytorchsentiment-analysistime-seriestime-series-anomaly-detectiontime-series-forecastingtransfer-learningtransformertutorialyolo