Sayak Paul
Machine Learning Street Talk (MLST)
00:00
Enhancing Knowledge Distillation
This chapter explores the advancements in knowledge distillation, particularly through ensemble teacher models and network pruning, while addressing the challenges posed by large datasets. It critically evaluates the balance between fine-tuning models, the risks of overfitting, and the ongoing evolution of generalization in advanced machine learning contexts.
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