Machine Learning Street Talk (MLST) Understanding Deep Learning - Prof. SIMON PRINCE [STAFF FAVOURITE]
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Dec 26, 2023 Simon Prince, a Professor at the University of Bath and author of 'Understanding Deep Learning', dives into the fascinating intricacies of deep learning. He discusses the surprising efficiency of deep learning models and the role of activation functions and architecture in their success. Notably, he challenges misconceptions surrounding overparameterization and the manifold hypothesis. The conversation also touches on ethical considerations in AI, the complexities of human cognition versus AI behavior, and the transformative impact of AlexNet on computer vision.
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Unreasonable Effectiveness of Deep Learning
- Deep learning models divide input space into convex polytopes, each with an affine function.
- AlexNet, with more parameters than data points, achieved surprising effectiveness despite potential overfitting.
Generalization Despite Overparameterization
- Data augmentation creates dependent, not independent, data points, affecting overparameterization analysis.
- Deep networks generalize better with more parameters, defying traditional statistical expectations like Rademacher complexity.
Manifold Hypothesis and Generative Models
- The manifold hypothesis suggests data lies in a lower-dimensional subspace, enabling generalization.
- Diffusion models' ability to generate diverse images with limited parameters supports this hypothesis.
