Artificial neural networks

Generative topographic map

Generative topographic map (GTM) is a machine learning method that is a probabilistic counterpart of the self-organizing map (SOM), is probably convergent and does not require a shrinking neighborhood or a decreasing step size. It is a generative model: the data is assumed to arise by first probabilistically picking a point in a low-dimensional space, mapping the point to the observed high-dimensional input space (via a smooth function), then adding noise in that space. The parameters of the low-dimensional probability distribution, the smooth map and the noise are all learned from the training data using the expectation-maximization (EM) algorithm. GTM was introduced in 1996 in a paper by Christopher Bishop, Markus Svensen, and Christopher K. I. Williams. (Wikipedia).

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High-precision map delivers a new picture of Antarctica's topography

Credit: NASA's Scientific Visualization Studio/DEMs provided by the Byrd Polar and Climate Research Center and the Polar Geospatial/Computer time provided through a Blue Waters Innovation Initiative/DEMs produced using data from DigitalGlobe, Inc.

From playlist Science Snippets

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Create a Triangle with Given Area: Quick Formative Assessment with GeoGebra

GeoGebra Resource: https://www.geogebra.org/m/gbcbbx29

From playlist Geometry: Dynamic Interactives!

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Mind maps: GeoGebra Notes New Feature!

🎉 #GeoGebra Notes new feature! 🎉 ➡️ Create your own mind map ➡️ Format any way you like! https://www.geogebra.org/notes

From playlist New Features and Releases

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How Does the Earth Create Different Landforms? Crash Course Geography #20

Cliffs and canyons, beaches and dunes, floodplains and river valleys, plateaus and mountains — these are all products of a restless Earth. In today’s episode we’re going to take a closer look at how landforms greatly influence how people live and derive meaning and a sense of place. From t

From playlist Geography

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Topographical mapping

This video lesson is part of a complete course on neuroscience time series analyses. The full course includes - over 47 hours of video instruction - lots and lots of MATLAB exercises and problem sets - access to a dedicated Q&A forum. You can find out more here: https://www.udem

From playlist NEW ANTS #1) Introductions

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Polar Coordinates: Dynamic Illustrator

Link: https://www.geogebra.org/m/guCcvwFP

From playlist Trigonometry: Dynamic Interactives!

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Surface Laplacian for cleaning, topoplogical localization, and connectivity

This lecturelet will introduce the surface Laplacian for cleaning and sharpening EEG data, and for preparation for connectivity analyses. The video uses files you can download from https://github.com/mikexcohen/ANTS_youtube_videos For more online courses about programming, data analysis,

From playlist OLD ANTS #6) Data pre-processing and cleaning

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Area of a Circle (by PEELING)!

GeoGebra resource: https://www.geogebra.org/m/WFbyhq9d

From playlist Geometry: Dynamic Interactives!

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Topographic VAEs learn Equivariant Capsules (Machine Learning Research Paper Explained)

#tvae #topographic #equivariant Variational Autoencoders model the latent space as a set of independent Gaussian random variables, which the decoder maps to a data distribution. However, this independence is not always desired, for example when dealing with video sequences, we know that s

From playlist Papers Explained

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Topographical plots

This video introduces how to interpret and create interpolated spatial distributions of electrode activity (a.k.a. topographical maps). The video uses files you can download from https://github.com/mikexcohen/ANTS_youtube_videos For more online courses about programming, data analysis, l

From playlist OLD ANTS #1) Introductions

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Introduction to Geometer's Sketchpad: Measurements

This video demonstrates some of the measurement and calculation features of Geometer's Sketchpad.

From playlist Geometer's Sketchpad

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Gov 2.0 Expo 2010: Jack Dangermond, "GeoEnabling Gov 2.0"

Jack Dangermond (ESRI), "GeoEnabling Gov 2.0"

From playlist Gov 2.0 Expo 2010

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Area within Polar Coordinates: Dynamic and Modifiable GeoGebra Illustrator

GeoGebra Resource Link: https://www.geogebra.org/m/mCuDhtjU

From playlist Calculus: Dynamic Interactives!

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!!Con 2018: Pseudofractals! Accidental aesthetics where math meets pixels by Jes Wolfe!

Pseudofractals! Accidental aesthetics where math meets pixels by Jes Wolfe! Computers have their own aesthetics, which come from their limitations. In this talk, I’ll show you a family of glitch art that is so fundamental to the idea of pixels that it has been independently rediscovered,

From playlist !!Con 2018

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Where 09: Rob Shanks, "ArcGIS and the Web: Better Sharing of Geographic Information"

Rob Shanks (ESRI) "ArcGIS and the Web: Better Sharing of Geographic Information"

From playlist Where 2.0 2009

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Are Those Your Lips or Feet? How Your Brain Rewires Itself After Amputation

From the top of your head to the bottom of your feet, your brain contains a map to your entire body. But what happens to that map if you lose a limb? Scientists Want to Transplant a Human Head, Here's Why That's a Bad Idea - https://youtu.be/CMHhF5N1FPY Get 20% off http://www.domain.com

From playlist Elements | Seeker

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Mapping Patagonia | Best Job Ever

Ross Donihue and Marty Schnure set out to create the first map of the future Patagonia National Park on a National Geographic Young Explorers grant. Their goal was to create an interactive map to give people an on-the-ground perspective of what it's like to stand in one of the most ecologi

From playlist Exploration | National Geographic

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Contour Lines

035 - Contour Lines In this video Paul Andersen explains how contour lines show areas of equal elevation or equal gravitational potential. Contour lines are used as an analogy for lines of equipotential in electric fields. Do you speak another language? Help me translate my videos: htt

From playlist AP Physics 2 Videos

Related pages

Neighbourhood (mathematics) | Self-organizing map | Connectionism | Neural network software | Latent variable model | Importance sampling | Nonlinear dimensionality reduction | Radial basis function network | Data mining | Generative model | Gaussian noise