061: Interpolation, Extrapolation and Linearisation (Prof. Yann LeCun, Dr. Randall Balestriero)
Machine Learning Street Talk (MLST)
Navigating the Complexities of Model Training
This chapter explores the critical aspects of training machine learning models, focusing on the necessity of sufficient data for effective noise reduction and the role of regularization techniques. It discusses the challenges of high-dimensional spaces and the importance of good parameterization and human engineering in enhancing model performance and generalization. Additionally, the chapter highlights the intricacies of interpolation and extrapolation in deep learning, emphasizing the relationship between geometric representations and data manifold dimensions.
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