This video is about how to predict the stock price of a company using a recurrent neural network. We will learn how to create our features and label and how to create a recurrent neural network using Keras.
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Robert Legenstein, Graz University of Technology
Computational Theories of the Brain
Machine Learning for Physics and the Physics of Learning 2019
Workshop IV: Using Physical Insights for Machine Learning
„Graph neural networks for combinatorial optimization problems“
Soledad Villar – New York University
Abstract: Graph neural networks are natural objects to express functions on graphs with relevant symmetries. In this talk we introduce graph neural networks, motivated by techniques in statistical physics. We explain how they are being used to learn algorithms for combinatorial optimization problems on graphs from data (like clustering, max-cut and quadratic assignment), in supervised and unsupervised manners. We also show a connection between universal approximation of invariant functions and the graph isomorphism problem.
Institute for Pure and Applied Mathematics, UCLA
November 21, 2019
For more information: http://www.ipam.ucla.edu/mlpws4 Video Rating: / 5