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02:05:34 MLPNS - Monte Carlo Markov Chain (MCMC) MLPNS - Monte Carlo Markov Chain (MCMC)	MLPNS - Monte Carlo Markov Chain (MCMC) Zoom Recording ID: 94326781852 UUID: DjQH89zcTACNQWieeu7TFA== Meeting Time: 2023-03-24 03:31:17pmGMT mcmc linear regression From Federica Bianco 0 likes 17 plays 0
03:04:28 MLPNS - linear regression and parameter… MLPNS - linear regression and parameter optimization	MLPNS - linear regression and parameter optimization Zoom Recording ID: 94326781852 UUID: k4s/9wkBSEas3w/zB43bSA== Meeting Time: 2023-03-23 02:27:12pmGMT machine learning linear regression From Federica Bianco 0 likes 10 plays 0
12:19 First order linear systems - Math 503 Fall 2022 First order linear systems - Math 503 Fall 2022	First order linear systems - Math 503 Fall 2022 Partial recording of lecture on linear first order systems linear first order phase portrait From Rakesh Rakesh 0 likes 9 plays 0 Department Name Mathematical Sciences Date Established November 10, 2022
56:03 Rakesh Rakesh's Personal Meeting Room Rakesh Rakesh's Personal Meeting Room	Rakesh Rakesh's Personal Meeting Room Zoom Recording ID: 2657638733 UUID: lIBlfKhvRMuoaaWFnVnkoA== Meeting Time: 2021-10-06 10:05:05pm linear algebra invertibility isomorphism From Rakesh Rakesh 0 likes 31 plays 0 Department Name Mathematical Sciences Date Established October 07, 2021
01:11:41 Designing Crack Propagation in Brittle Composites Designing Crack Propagation in Brittle Composites	Designing Crack Propagation in Brittle Composites Presenters Dr. Zubaer Hosszin Fazle Elahi Tousif Ahmed Tengyuan Hao Zoom Recording ID: 92787051593 UUID: wsjBy10ESYGpKExPnZ5YBg== Meeting Time: 2021-06-24 09:51:34pm interface toughness crack deflection energy material length carbon strength fracture scale point behavior silicon linear nanotube simulation side materials things From Kristen Scully 0 likes 12 plays 0 Department Name Center for Composite Materials Department Division Engineering Date Established June 24, 2021
06:14 VII.3 Separable equations VII.3 Separable equations	VII.3 Separable equations separable differential equation solution sides times left equation a. problem x. function form note t. restrictions constant equations linear c. condition formula lot From Tobin Driscoll 0 likes 74 plays 0
01:48:09 Neural Networks 101 - deepdream Neural Networks 101 - deepdream	Neural Networks 101 - deepdream 702507326 network layer image function networks layers model input training parameters output loss things linear perceptron learning weights hyperparameters problem activation From Federica Bianco 0 likes 37 plays 0
11:29 Analysis and Simulation of a Novel Run-and-Tumble… Analysis and Simulation of a Novel Run-and-Tumble Model with Autochemotaxis, Nicholas Russell	Analysis and Simulation of a Novel Run-and-Tumble Model with Autochemotaxis, Nicholas Russell We model, analyze, and simulate a novel run-and-tumble model with autochemotaxis, biologically inspired by the phytoplankton, Heterosigma akashiwo. Developing a fundamental understanding of… mathematics modeling collective behavior plankton [email protected] mathematical sciences chemical function thing distribution aggregation delta light model variation structure gradient switch lot f1 f2 linear aggregations change intensity From Lauren Mosesso 0 likes 12 plays 0 Department Name Mathematical Sciences
06:26 VI.4 Homogeneous solutions VI.4 Homogeneous solutions	VI.4 Homogeneous solutions differential equation matrix times wronskian vector solution t. determinant equation column definition columns capital solutions problem functions c. condition linear combination things From Tobin Driscoll 0 likes 78 plays 0
04:18 VI.3 Solution structure VI.3 Solution structure	VI.3 Solution structure differential equation equation vector a. dependence variables matrix x3 x2 kmb components degrees attic basement function forcing coefficient linear coefficients ax equals From Tobin Driscoll 0 likes 76 plays 0
08:34 VI. 1-2 Order and dimension VI. 1-2 Order and dimension	VI. 1-2 Order and dimension differential equation prime equation vector definition variable variables equations problems linear unknowns matrix derivatives theta space a. u. dimensions u5 u4 u3 From Tobin Driscoll 0 likes 84 plays 0
01:29:35 MLPNS - ML and linear regression 2 MLPNS - ML and linear regression 2	MLPNS - ML and linear regression 2 702507326 model function gradient learning chi linear machine probability point likelihood square problem process solution parameters l2 minimum law sigma error From Federica Bianco 0 likes 22 plays 0
01:59:03 MLPNS - ML and linear regression 1 MLPNS - ML and linear regression 1	MLPNS - ML and linear regression 1 702507326 machine model learning analysis regression test distribution parameters variable variables function lot things linear input prediction distance law square sample From Federica Bianco 0 likes 34 plays 0
12:56 IV.6 Free oscillators IV.6 Free oscillators	IV.6 Free oscillators oscillators theta root frequency omega coefficient times roots cosine solution m. oscillator equation problem ode linear halves form sine parameters equilibrium From Tobin Driscoll 0 likes 132 plays 0
11:30 IV. 1-2 Structure of 2nd-order equations IV. 1-2 Structure of 2nd-order equations	IV. 1-2 Structure of 2nd-order equations differential equation problem solution times linear x2 prime t. function form constants problems equations q. thing equation u. reduction definition operator functions From Tobin Driscoll 0 likes 98 plays 0
08:27 II.7 IVP formula II.7 IVP formula	II.7 IVP formula differential equations solution tilde response times equation condition c1 sum problems formula problem integrand exponent tilda g. coefficient linear structure forcings responses From Tobin Driscoll 0 likes 122 plays 0