Pattern Causality: Revealing timeseries interdependecies through attractor reconstruction Published 2019-05-14 Download video MP4 360p Recommendations 22:29 From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction 15:41 What if Singularities DO NOT Exist? 01:51 Constructing Empirical Dynamic Models: Taken' Theorem 25:16 Time Delays for Model Discovery 12:37 Nonlinear Dynamics: Delay Coordinate Embedding 06:28 Principal Component Analysis (PCA) 17:36 The Discrete Fourier Transform (DFT) 57:38 Keynote Address: George Sugihara, MS, PhD 10:58 Convolution Operation in CNN 04:30 Nonlinear Dynamics: Topology, Diffeomorphisms, and Reconstruction of Dynamics 21:46 Transforming Signals to Images Using Attractor Reconstruction for Deep Learning 14:31 Population and Estimated Parameters, Clearly Explained!!! 15:05 Variational Autoencoders 09:40 Tensors for Neural Networks, Clearly Explained!!! 12:51 Singular Value Decomposition (SVD): Mathematical Overview 48:51 15. Projections onto Subspaces 13:16 Time Series Talk : Autocorrelation and Partial Autocorrelation 07:47 Covariance Clearly Explained! 20:33 Gradient descent, how neural networks learn | Chapter 2, Deep learning Similar videos 1:16:07 INFORMS TutORials - Mining Nonlinear Dynamics In Operational Data For Process Improvement 1:14:58 C. C. Mei Distinguished Speaker Series Spring 2017: Prof. George Sugihara 1:16:07 Mining Nonlinear Dynamics In Operational Data For Process Improvement 1:39:36 Rupert Sheldrake on the Influence of A. N. Whitehead 50:50 Domain ReCommoning: Platform Thinking with the Three Economies - Jabe Bloom - DDD Europe 2:18:10 Brain Rhythms: Understanding, Measurement, Analysis and Applications (16IST16) 22.06.2016 2nd Half 1:10:50 John Vervaeke Q&A (May 30, 2022) 2:06:13 TheNielsBohrLectures2018: Jaan Valsiner: Lecture2 1:55:50 SESSION 14 - COMPLEXITY ECONOMICS More results