OpenAI

Related lectures (26)

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Explores canonical embedding of #-fields, lattices, and finiteness of class groups.

Covers the Lagrange polynomial interpolation method and error analysis in function approximation.

Covers Rolle's Theorem and the Mean Value Theorem, discussing stationary points and extrema.

Explores the Thomas algorithm for tridiagonal systems and the accuracy of direct methods in numerical computations.

Delves into value tensions in AI ethics, balancing inclusion and privacy.

Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.

Covers real number sequences, including convergence and boundedness concepts.

Explores advancements in generative AI and reinforcement learning, focusing on their applications, safety, and future research directions.

Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.