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03:28 Johannes Buck EVA Talk Preview Johannes Buck EVA Talk Preview	Johannes Buck EVA Talk Preview This talk has captions. You can remove these by pressing CC on the video toolbar. Name: Johannes Buck Talk Title: Properties and Consistency of QTree in Max-Linear Models Under Observational Noise… icms eva solution theory model properties consistency max-linear noise river networks tree equation distribution edge linear minimum score max edges log lock assumptions gap algorithm bit estimator outputs theorem From Anna Munro on May 24th, 2021 0 likes 13 plays 0 Licence Type All rights reserved Language English
57:00 ASR Lecture 10 (part 2); Lecture 11 ASR Lecture 10 (part 2); Lecture 11	ASR Lecture 10 (part 2); Lecture 11 ASR Lecture 10 (part 2): Neural network models (1); ASR Lecture 11: Neural network models (2) speech recognition network networks probability bit speech recognition features output layer error weights outputs label delta things function hmm term lecture layers From Peter Bell on March 25th, 2021 0 likes 67 plays 0 Licence Type Creative Commons - Attribution Non Commercial Date Created February 22nd, 2021
13:16 AVLSI Demo 10 - Stability Probes AVLSI Demo 10 - Stability Probes	AVLSI Demo 10 - Stability Probes How to use stability probes to check stability, phase margin etc. stability cadence virtuoso avlsia analogue circuits analog circuits phase analysis amplifier plots margin plot gain probe points differential loop signal phi outputs thing frequency response things mode From Filip Taneski on March 4th, 2021 0 likes 3 plays 0 Course Code ELEE11073 Publisher Filip Taneski Licence Type Creative Commons - Attribution Language English Date Created March 4th, 2021
47:46 ASR Lecture 9 part I ASR Lecture 9 part I	ASR Lecture 9 part I ASR Lecture 9: WFSTs for ASR (partial lecture due to technical issues) speech recognition transducer probability bit output symbol weights probabilities point composition word path log input sequence weight pizza outputs grandma recognition loop From Peter Bell on February 15th, 2021 0 likes 111 plays 0 Licence Type Creative Commons - Attribution Non Commercial Date Created February 8th, 2021
52:44 Agnieszka Borowska (Glasgow): Gaussian process… Agnieszka Borowska (Glasgow): Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis	Agnieszka Borowska (Glasgow): Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis Agnieszka Borowska (Glasgow): Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis Chemotaxis is… statistics research parameters cell parameter abc regression features estimates model outputs points element point membrane process simulator step cases nodes instance distribution From OLLIE Quinn on February 4th, 2021 0 likes 18 plays 0 Licence Type All rights reserved Language English Date Created January 29th, 2021
07:29 AVLSI Demo 4 - Calculator Basics and Creating… AVLSI Demo 4 - Calculator Basics and Creating DCOP Expression	AVLSI Demo 4 - Calculator Basics and Creating DCOP Expression Basics of using the Cadence Virtuoso calculator and adding DCOP expressions to the ADE output avlsib analogue circuits cadence virtuoso device expression stack calculator outputs point characteristics devices margin millivolts vsat ad cursor thing buffer vds parameter step simulation voltage From Filip Taneski on January 28th, 2021 0 likes 2 plays 0 Course Code ELEE11073 Publisher Filip Taneski Licence Type Creative Commons - Attribution Language English Date Created January 28th, 2021
02:56 Distillation Lecture 1 2020 part 3 Distillation Lecture 1 2020 part 3	Distillation Lecture 1 2020 part 3 Distillation Lecture 1 2020 part 3 column purity bottom specifications trays disposal tools question analysis input energy specification sense streams output outputs concentration ideal inefficiencies From John Christy on December 24th, 2020 0 likes 50 plays 0 Course Code CHEE11023 Publisher John Christy Licence Type All rights reserved The University of Edinburgh Language British English Date Created December 24th, 2020
11:13 ML7: Logistic Classification ML7: Logistic Classification	ML7: Logistic Classification Here, we introduce a logistic classifier and cover how it differs from a linear classifier. Then, we show how its parameters can be learnt by minimising the cross-entropy loss. Finally, we visualise… classifier loss class function probability points point parameters label outputs i. training linear boundary decision side contribution times minus thing From Elliot Crowley on November 6th, 2020 0 likes 10 plays 0
02:39 MLP Lecture 07 - Clip 08 - Learning Image Kernels MLP Lecture 07 - Clip 08 - Learning Image Kernels	MLP Lecture 07 - Clip 08 - Learning Image Kernels Machine Learning Practical (MLP) Lecture 07, Clip 08 / 09. machine learning deep learning image kernels cat pixels kernel feature transformation hierarchy weight grayscale detection edge values contrast outputs input layer net training examples From Pavlos Andreadis on November 2nd, 2020 1 likes 171 plays 0 Course Code INFR11132 Licence Type All rights reserved Language English
08:51 MLP Lecture 06 - Clip 02 - Weight Initialization MLP Lecture 06 - Clip 02 - Weight Initialization	MLP Lecture 06 - Clip 02 - Weight Initialization Machine Learning Practical (MLP) Lecture 06, Clip 02 / 05. machine learning deep learning variance distribution weights units output input backpropagation inputs range practice network hour idea outputs beginning problem deviation initialization root expectation From Pavlos Andreadis on October 24th, 2020 1 likes 191 plays 0 Course Code INFR11132 Licence Type All rights reserved Language English
20:21 2b - Six Sigma and DMAIC 2b - Six Sigma and DMAIC	2b - Six Sigma and DMAIC Lecture 2b - Six Sigma and DMAIC by Prof Ian Underwood engineering design and manufacture problem plan failures failure customer process analysis root team rule tools implant solution control improvement lot end outputs scope experiments From Lesley Gibson on September 3rd, 2020 0 likes 79 plays 0 Licence Type Creative Commons - Attribution
01:27 Generalization in Machine Learning Generalization in Machine Learning	Generalization in Machine Learning Generalization in Machine Learning point predict labels number instances targets interesting decision system regression build algorithms evaluation accuracy iaml training data tomorrow outputs reason train effects percent accuracy predictor matter future values attributes classifier training classifiers neighbor trees level app model From Nigel Goddard on September 20th, 2016 0 likes 3,014 plays 0 Licence Type Creative Commons - Attribution
01:19 Least Squares Classification Least Squares Classification	Least Squares Classification Least Squares Classification burst predict produced magenta regression aggression caption complete serum regression analysis points iaml spell linear loose drives reason produce complicated effects sound great outlaw linear regression optimization logistic regression action output problem logistic procedure outputs outliers effect decision squares shows green ascent gradient bit From Nigel Goddard on September 8th, 2016 0 likes 3,227 plays 0 Licence Type Creative Commons - Attribution

Search for tag: "outputs"

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