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#64 Prof. Gary Marcus 3.0
Machine Learning Street Talk (MLST)
Exploring Maximum Likelihood and Beyond in Neural Networks
This chapter explores the complexities of training neural networks through maximum likelihood estimation and its impact on model performance. The speakers discuss key concepts like double descent and grokking while comparing maximum likelihood behavior to Bayesian model averaging in terms of generalization.
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