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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 on November 2nd, 2020 1 likes 176 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 on June 8th, 2020 0 likes 77 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 on April 5th, 2019 0 likes 101 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 on September 25th, 2016 1 likes 1,871 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 on September 25th, 2016 1 likes 1,535 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 on September 25th, 2016 2 likes 2,124 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 on September 25th, 2016 1 likes 1,841 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 on September 25th, 2016 2 likes 1,851 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 on September 25th, 2016 2 likes 1,858 plays 0 Licence Type Creative Commons - Attribution
02:19 Pros and cons of nearest-neighbor methods Pros and cons of nearest-neighbor methods	Pros and cons of nearest-neighbor methods Pros and cons of nearest-neighbor methods iaml nearest neighbours training algorithm examples testing trees decision sets operations instance comparisons attributes lots values instances thing test estimates dimensionality times bit From Nigel Goddard on September 21st, 2016 1 likes 2,526 plays 0 Licence Type Creative Commons - Attribution

Search for tag: "dimensionality"

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