Top Repositories
Learn how to develop, deploy and iterate on production-grade ML applications.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
🏃 Implementation of Using Fast Weights to Attend to the Recent Past.
Construct a modern data stack and orchestration the workflows to create high quality data for analytics and ML applications.
Learn how to monitor ML systems to identify and mitigate sources of drift before model performance decay.
Learn how to create reliable ML systems by testing code, data and models.
Repositories
15Learn how to develop, deploy and iterate on production-grade ML applications.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Learn how to monitor ML systems to identify and mitigate sources of drift before model performance decay.
🏃 Implementation of Using Fast Weights to Attend to the Recent Past.
💤 Old repository of notes on machine learning papers.
Construct a modern data stack and orchestration the workflows to create high quality data for analytics and ML applications.
Using a feature store to connect the DataOps and MLOps workflows to enable collaborative teams to develop efficiently.
Learn how to create reliable ML systems by testing code, data and models.
📓 Notes from The Neural Perspective (discontinued) blog.
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🌍 Configuration files for Jupyter features.
🔍 Attentional interfaces in TensorFlow.
🔥 Introductory PyTorch tutorials with OReilly Media.
🤖 Implementation of Self Normalizing Networks (SNN) in PyTorch.
No description provided.