061: Interpolation, Extrapolation and Linearisation (Prof. Yann LeCun, Dr. Randall Balestriero)
Machine Learning Street Talk (MLST)
Dimensionality in Machine Learning: MNIST vs. ImageNet
This chapter explores the challenges posed by different datasets in machine learning, focusing on MNIST and ImageNet. It examines how dimensionality affects interpolation and extrapolation, highlighting the role of principal component analysis (PCA) in understanding data variance. The discussion emphasizes the significance of image resolution and data representation in improving classification efficiency and approach.
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