Examples applications of Oja's learning rule

Related lectures (29)

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Covers the basics of Convolutional Neural Networks, including training optimization, layer structure, and potential pitfalls of summary statistics.

Explores adaptive gradient methods, their properties, convergence, and comparison with traditional optimization algorithms.

Covers statistical physics tools for optimization, learning, graph coloring, recommendation systems, and neural networks.

Delves into structures in non-convex optimization, emphasizing scalable optimization for deep learning.

Explores the deep learning paradigm, including challenges, neural networks, robustness, fairness, interpretability, and energy efficiency.

Introduces supervised learning using labeled data points to optimize classifier output.

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

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Explores stochastic gradient descent optimization and the Mean-Field Method in neural networks.