Search for tag: "dimensionality"

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33:43 AML: Non-Linear Dimensionality Reduction -… AML: Non-Linear Dimensionality Reduction - Visualisation	AML: Non-Linear Dimensionality Reduction - Visualisation Applied Machine Learning - INFR11211 AML: Non-Linear Dimensionality Reduction - Visualisation Part 2/2 aml machine learning dimensionality reduction visualisation space distance points embedding graph manifold measure point distances distribution neighbours linear dataset things weights methods reduction dimensionality iso sne From Siddharth N 0 likes 86 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
11:03 AML: Non-Linear Dimensionality Reduction -… AML: Non-Linear Dimensionality Reduction - Extending Linear Methods	AML: Non-Linear Dimensionality Reduction - Extending Linear Methods Applied Machine Learning - INFR11211 AML: Non-Linear Dimensionality Reduction - Extending Linear Methods Part 1/2 aml machine learning nonlinear dimensionality reduction function kernel vector pca linear space points basis matrix values phi feature dimensions dimensionality eigenvector kind conditions condition covariance dataset From Siddharth N 0 likes 78 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
15:22 AML: Exploratory Data Analysis - PCA Examples AML: Exploratory Data Analysis - PCA Examples	AML: Exploratory Data Analysis - PCA Examples Applied Machine Learning - INFR11211 AML: Exploratory Data Analysis - PCA Examples Part 3/3 aml machine learning exploratory data analysis pca component dimensions variance direction components linear features axis directions dimensionality outliers wales dimension points principal bca fact e.g. scotland From Siddharth N 0 likes 126 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
07:38 AML: Exploratory Data Analysis - Data… AML: Exploratory Data Analysis - Data Visualisation	AML: Exploratory Data Analysis - Data Visualisation Applied Machine Learning - INFR11211 AML: Exploratory Data Analysis - Data Visualisation Part 1/3 aml machine learning exploratory data analysis visualisation components features variables ways dimensionality things plot e.g. charts domain digits images space plots component options dimensions construction knowledge decision From Siddharth N 0 likes 135 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
09:47 Anis Hamadouche Presentation - SSPD 2021 Anis Hamadouche Presentation - SSPD 2021	Anis Hamadouche Presentation - SSPD 2021 Presented at the Sensor Signal Processing for Defence Conference (SSPD) 2021Presentation: "Approximate Proximal-Gradient Methods" Speaker: Anis Hamadouche, Heriot-Watt… sspd 2021 error bounds rate convergence errors table analysis approximations precision algorithm function gradient bound assumptions problems optimization application step vector dimensionality From Joe Burchell 0 likes 31 plays 0 Licence Type All rights reserved Language English Date Created September 15th, 2021
03:00 Edossa Merga Terefe EVA Talk Preview Edossa Merga Terefe EVA Talk Preview	Edossa Merga Terefe EVA Talk Preview This talk has captions. You can remove these by pressing CC on the video toolbar. Name: Edossa Merga Terefe Talk Title: Extremal Random Forests Abstract: Methods from statistics and machine… icms eva theory solution model data extremal random forests methods statistics machine learning quantiles function quantile space predictor models algorithm idea range dimensionality picture level variable response detail conference cantilever philip levels erf competitor From Anna Munro 0 likes 27 plays 0 Licence Type All rights reserved Language English
08:32 ML2: Dimensionality Reduction ML2: Dimensionality Reduction	ML2: Dimensionality Reduction In this second video, we motivate why it is useful to reduce the dimensionality of your data, and describe how this can be done linearly, using a transformation matrix. We describe a setup whereby we… matrix reconstruction squiggle points dimensionality transpose transformation point column dataset reconstructions distance era columns dimensions inverse rows dimension x. distances From Elliot Crowley 0 likes 20 plays 0
06:35 MLP Lecture 07 - Clip 06 - Multiple Feature Maps MLP Lecture 07 - Clip 06 - Multiple Feature Maps	MLP Lecture 07 - Clip 06 - Multiple Feature Maps Machine Learning Practical (MLP) Lecture 07, Clip 06 / 09. machine learning deep learning times feature maps channels input image kernel size units kernels channel output matrices parameters 3-dimensions dimension dimensionality connections bias capital From Pavlos Andreadis 1 likes 488 plays 0 Course Code INFR11132 Licence Type All rights reserved Language English
05:31 Unsupervised Learning Unsupervised Learning	Unsupervised Learning Unsupervised Learning hds dataset learning cluster clusters features pca component algorithm instance components variance direction reduction assignment point dimensions words dimensionality centers points From Dimitrios Doudesis 0 likes 377 plays 0 Course Code GLHE11086 Licence Type Creative Commons - Attribution Share A Like
04:53 Sampling Sampling	Sampling Sampling - Week 4 homogeneous computer data predictive analytics micromasters presence trend random sampling sample top-level domains techniques dimensionality positive point emphasize ensure caption complete approach hiv furthest selecting hiv positive effects dataset distance training scientific method observation data set length algorithms limited number replacement From Calum Macphail 0 likes 118 plays 0 Course Code PA1.2x Evaluation of Predictive Modelling Licence Type Creative Commons - Attribution Share A Like Language English
01:59 Pros and cons of dimensionality reduction Pros and cons of dimensionality reduction	Pros and cons of dimensionality reduction Pros and cons of dimensionality reduction iaml pca space classification bayes dimensionality dimensions lot reason harm classes goal sets algorithms class downside upside assumption independence classifier classifiers From Nigel Goddard 1 likes 2,643 plays 0 Licence Type Creative Commons - Attribution
03:56 How many principal components to use How many principal components to use	How many principal components to use How many principal components to use iaml pca variance eigenvalues sum dimensions lambda eigenvectors point dimensionality graph space decrease elbow inflection plot means scri sentences numbers sense humans From Nigel Goddard 1 likes 2,150 plays 0 Licence Type Creative Commons - Attribution
02:59 Why we maximize variance in PCA Why we maximize variance in PCA	Why we maximize variance in PCA Why we maximize variance in PCA iaml pca dimension points space variance projection distances point dimensions e. dimensionality step weight cases channel algorithms basis height colors dataset thing From Nigel Goddard 2 likes 2,961 plays 0 Licence Type Creative Commons - Attribution
03:59 Tackling the curse of dimensionality Tackling the curse of dimensionality	Tackling the curse of dimensionality Tackling the curse of dimensionality iaml pca lot attributes regions assumption space attribute independence histograms observations domain features dimensionality symmetry thing cells neighboring probability distributions smoothness ability From Nigel Goddard 1 likes 2,576 plays 0 Licence Type Creative Commons - Attribution
02:47 The curse of dimensionality The curse of dimensionality	The curse of dimensionality The curse of dimensionality iaml pca space regions observations examples points attribute training times problem dimensionality lots dataset decision curse inference zeros cells lot accounts dimensions From Nigel Goddard 2 likes 2,567 plays 0 Licence Type Creative Commons - Attribution
03:54 Data manifolds in high-dimensional spaces Data manifolds in high-dimensional spaces	Data manifolds in high-dimensional spaces Data manifolds in high-dimensional spaces iaml pca space digits dimensionality pixels structure text pen digit examples things binary bitmaps point points lots attribute dimensions images rendering stroke From Nigel Goddard 2 likes 2,554 plays 0 Licence Type Creative Commons - Attribution

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