Interpretability in ML
Suchin and Taylor discuss the importance of interpretability in machine learning, emphasizing the need for models to provide explanations for their predictions. They delve into the challenges of balancing interpretability with the power of neural networks, highlighting the significance of understanding assumptions in historical data analysis.In this clip
From this podcast
NLP Highlights
97 - Automated Analysis Of Historical Printed Documents, With Taylor Berg-Kirkpatrick
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