Replica computation and machine learning

Related lectures (31)

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Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Explores the statistical mechanics of learning, focusing on neural networks' mysteries and computational challenges.

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

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Explores machine learning through solvable models, covering sample complexity, neural networks, and computational gaps.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Delves into machine learned interatomic potentials, showcasing their accuracy and cost-effectiveness in predicting chemical properties.

Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.