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Made-With-ML
Learn how to responsibly develop, deploy and maintain production machine learning applications.mlops-course
Learn how to design, develop, deploy and maintain an end-to-end ML application at scale.fast-weights
π Implementation of Using Fast Weights to Attend to the Recent Past.data-engineering
Construct a modern data stack and orchestration the workflows to create high quality data for analytics and ML applications.the-neural-perspective
π Notes from The Neural Perspective (discontinued) blog.casual-digressions
π€ Old repository of notes on machine learning papers.attentional-interfaces
π Attentional interfaces in TensorFlow.testing-ml
Learn how to create reliable ML systems by testing code, data and models.oreilly-pytorch
π₯ Introductory PyTorch tutorials with OReilly Media.monitoring-ml
Learn how to monitor ML systems to identify and mitigate sources of drift before model performance decay.feature-store
Using a feature store to connect the DataOps and MLOps workflows to enable collaborative teams to develop efficiently.GokuMohandas
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SELU
π€ Implementation of Self Normalizing Networks (SNN) in PyTorch.Love Open Source and this site? Check out how you can help us