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Related lectures (31)

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Explores learning challenges with distribution shifts and perturbation geometry, focusing on robust classifiers and natural variation modeling.

Explores the generalization of ImageNet classifiers and the challenges in achieving reliable machine learning models.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Explores Convolutional Neural Networks, data augmentation, weight decay, and dropout for enhancing model performance.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.