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Orbifold Consulting – Think. Understand. Visualize.

Orbifold is a one-person consulting company focusing on data science, business intelligence (machine learning, graph mining, data visualization, dashboards), mathematical and algorithmic techniques. For mobile, web, desktop and cloud.

http://www.orbifold.net/

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Orbifold is a one-person consulting company focusing on data science, business intelligence (machine learning, graph mining, data visualization, dashboards), mathematical and algorithmic techniques. For mobile, web, desktop and cloud.
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Orbifold Consulting – Think. Understand. Visualize. | orbifold.net Reviews

https://orbifold.net

Orbifold is a one-person consulting company focusing on data science, business intelligence (machine learning, graph mining, data visualization, dashboards), mathematical and algorithmic techniques. For mobile, web, desktop and cloud.

INTERNAL PAGES

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1

Orbifold Consulting

http://www.orbifold.net/default/portfolio

Neo4j client using yFiles for HTML. MongoDB client based on yFiles for HTML. Virus tracking and Xamarin development. Combining 2D and 3D dataviz. Analyzing and visualizing graph data with F#. Categories, monads and F#. A kaleidoscope from F# and monads. A functional graph library. A financial dashboard designer. Community detection; F# vs. Mathematica. Ten years of diagramming. Diagramming scalability and performance. Numerics and math library for .Net, Mac, Xamarin and iOS. Discrete calculus on graphs.

2

Orbifold Consulting

http://www.orbifold.net/default/numerics

An open source library for .Net and Swift full of data structures, functions, methods and mathematical utilities covering finance, statistics, engineering, text analysis, clustering, data mining and more. Numerics for .Net.

3

Orbifold Consulting

http://www.orbifold.net/default/graph-databases

The shortlist of graph databases I keep an eye on. Many other NoSQL engines can store graph-like data, of course. Neo4j is a graph database boasting massive performance improvements versus relational databases. It is very agile and fast. At the moment it is used by many startups in applications such as social platforms, fraud detection, recommendation engines etc. The data is stored in nodes that are connected by directed, typed relationships with properties of both (a property graph). There is no other ...

4

Orbifold Consulting

http://www.orbifold.net/default/zeromq-javascript-client

ZeroMQ WebSocket JavaScript client. This is about one of those projects where you have to dig into some new cool technology and wonder what the buzz is about. ZeroMQ. And all its flavors is about using queueing and web sockets to create pubsub integrations with, in my case, NetMQ. As a service and a browser as a client (using jsMQ. Really pleasantly surprised how easy it is to push data to the browser and even ‘stream’ images to subscribers. On the server an MQ service publishes a stream of images like so.

5

Orbifold Consulting

http://www.orbifold.net/default/twitter-in-r

I am a Telerik Expert. Tutorial: Markov chain in R/C. A year of R-esearch. R integration in SQL Server 2016. Classification, dimensional reduction and chi2. Stochastic integrals via R. Monads (with snippets in R and Swift). Time series in R. YFiles for HTML with ASP.Net MVC backend. Docker, JavaScript and R. ZeroMQ WebSocket JavaScript client. Time series moving average in T-SQL. WebStorm NodeJS to Azure. Time series analysis functions to remember. TypeScript, Angular and VS Code. Azure ML cheat sheet.

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numerics.io numerics.io

Tip Archives - Algorithms | Science | Code

http://numerics.io/category/tip

Numerics library for .Net. July 2, 2014. There is definitely as lot of fun in the new Mathematica 10 and the Cloud. Machine learning is so easy to apply, it’s stunning. At the same time, the magic does not make the output automatically true. A quick and simple test reveals some fun facts;. Great fun at the swimming pool". C'est plus beau quand c'est inutile". Stairs have no orientation". My daughter left early today". This black-box classification should have been amplified with at least some hints about...

numerics.io numerics.io

Katz centrality - Algorithms | Science | Code

http://numerics.io/katz-centrality

Numerics library for .Net. April 3, 2014. Take an arbitrary network, preferably a directed one, and ask yourself; what is the most important node in this network? The question appears in all sorts of contexts; data networks (what is the critical hub? Social networks (who is the influential person? Epidemic networks (who is spreading the virus? Take a node and assign to the adjacent node a weight. To the next level of children/siblings assign. And so on. In other words. Is the largest eigenvalue of A we d...

numerics.io numerics.io

1001 ways to create graphs in Mathematica - Algorithms | Science | Code

http://numerics.io/1001-graphs

Numerics library for .Net. 1001 ways to create graphs in Mathematica. September 3, 2014. This is an overview of the many ways in which one can create graphs in Mathematica. It demonstrates the ubiquity of graphs and how Mathematica. Makes it easy to experiment with ideas. At the same time, various Mathematica. Symbols and techniques are presented which we will cover more in depth in later article. And unwrap its faces. The number of vertices can be found in two ways here, either using the VertexCount.

numerics.io numerics.io

Tip: some classification - Algorithms | Science | Code

http://numerics.io/classification-issues

Numerics library for .Net. July 2, 2014. There is definitely as lot of fun in the new Mathematica 10 and the Cloud. Machine learning is so easy to apply, it’s stunning. At the same time, the magic does not make the output automatically true. A quick and simple test reveals some fun facts;. Great fun at the swimming pool". C'est plus beau quand c'est inutile". Stairs have no orientation". My daughter left early today". This black-box classification should have been amplified with at least some hints about...

numerics.io numerics.io

Numerics, Author at Algorithms | Science | Code

http://numerics.io/author/numerics

Numerics library for .Net. All posts by Numerics. Time series in R. October 17, 2015. April 10, 2015. Gradient boosting machine learning. March 2, 2015. The similarity with a basis of functions in an Hilbert space is sometimes difficult to push aside. If we have weak learners. Then an exact learner would be. But you immediately face the questions. What kind of space is F sitting in. What is the inner-product in this space which articulates the basis. An approximation F of the full function. Makes it easy...

numerics.io numerics.io

Gradient boosting machine learning - Algorithms | Science | Code

http://numerics.io/gradient-boosting

Numerics library for .Net. Gradient boosting machine learning. March 2, 2015. The similarity with a basis of functions in an Hilbert space is sometimes difficult to push aside. If we have weak learners. Then an exact learner would be. But you immediately face the questions. What kind of space is F sitting in. What is the inner-product in this space which articulates the basis. How would one in general reformulate the analogy within category theory. An approximation F of the full function. Time series in R.

numerics.io numerics.io

Representing graphs in F# - Algorithms | Science | Code

http://numerics.io/fsharp-graphs

Numerics library for .Net. Representing graphs in F#. April 3, 2014. From a purely mathematical point of view you can idealize nodes and connections but from a more practical point of view it’s useful to associate to a node some payload or data:. Some concrete data which adds a dimension to the node related to the business context. For example, you could attach Twitter user information or a geographic tag or even an image. We will define in F# a node as a combination of these two elements:. A connection,...

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Orbifold Consulting – Think. Understand. Visualize.

Data Science is a combination of. Business, software, maths, visualization and communication. Data Science and Analytics. 25 years of expertise. Including graph layout and graph visuaization techniques. Custom widgets in JavaScript. Angular, Kendo, React. MEAN all the way. Artificial Intelligence as a Service. Natural Language Understanding, Bots, Personalized Semantic Networks, Neural Networks and more. Data Science and Analytics. Graph analytics and diagramming. We wrote graph algorithms and implemente...

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