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Foundations of supervised and unsupervised learning with practical Python labs.
Hands-on CNNs, RNNs, transfer learning and model deployment examples.
Engineering best practices: model versioning, CI, monitoring, and scaling.
Short, practical tutorials that get you coding fast.
Balanced image sets for classification experiments with labels and sample notebooks.
One-click demos and Binder/Colab-ready notebooks to try models instantly.
Train a convolutional model end-to-end on example dataset.
Fine-tune a transformer for text classification with transfer learning.
Simulate drift and visualize model metrics and alerts.
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