Owl - An OCaml Numerical Library

Owl - An OCaml Numerical Library

Liang Wang

Liang Wang

Cambridge, England

Owl is an OCaml library for scientific computing and machine learning using functional programming

Artificial Intelligence

Description

Owl is an OCaml library for scientific computing. It enables academic researchers to fast prototype machine learning algorithms and construct deep neural networks with very concise code. It also facilitates industry programmers to develop robust analytical applications using functional language at a large scale.

Currently, Owl supports N-dimensional arrays, both dense and sparse matrix operations, linear algebra, regressions, fast Fourier transforms, and many advanced mathematical and statistical functions (such as Markov chain Monte Carlo methods). Recently, Owl has also implemented algorithmic differentiation, on top of which we have been building a powerful deep neural network module. With its advanced underlying distributed computation engine, Owl is able to support distributed computing to take advantage of the computation power in a computer cluster.

Owl is under active development. It aims to provide a comprehensive set of advanced numerical functions to enable fast development of robust and efficient analytical applications.

Github: https://github.com/ryanrhymes/owl

Gallery

Links

Owl's Github Repository

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Liang W. added photos to project Owl - An OCaml Numerical Library

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Owl - An OCaml Numerical Library

Owl is an OCaml library for scientific computing. It enables academic researchers to fast prototype machine learning algorithms and construct deep neural networks with very concise code. It also facilitates industry programmers to develop robust analytical applications using functional language at a large scale.

Currently, Owl supports N-dimensional arrays, both dense and sparse matrix operations, linear algebra, regressions, fast Fourier transforms, and many advanced mathematical and statistical functions (such as Markov chain Monte Carlo methods). Recently, Owl has also implemented algorithmic differentiation, on top of which we have been building a powerful deep neural network module. With its advanced underlying distributed computation engine, Owl is able to support distributed computing to take advantage of the computation power in a computer cluster.

Owl is under active development. It aims to provide a comprehensive set of advanced numerical functions to enable fast development of robust and efficient analytical applications.

Github: https://github.com/ryanrhymes/owl

Liang wang small

Liang W. added photos to project Owl - An OCaml Numerical Library

Medium ef26c94a d8ad 4612 8c66 f77db9ade265

Owl - An OCaml Numerical Library

Owl is an OCaml library for scientific computing. It enables academic researchers to fast prototype machine learning algorithms and construct deep neural networks with very concise code. It also facilitates industry programmers to develop robust analytical applications using functional language at a large scale.

Currently, Owl supports N-dimensional arrays, both dense and sparse matrix operations, linear algebra, regressions, fast Fourier transforms, and many advanced mathematical and statistical functions (such as Markov chain Monte Carlo methods). Recently, Owl has also implemented algorithmic differentiation, on top of which we have been building a powerful deep neural network module. With its advanced underlying distributed computation engine, Owl is able to support distributed computing to take advantage of the computation power in a computer cluster.

Owl is under active development. It aims to provide a comprehensive set of advanced numerical functions to enable fast development of robust and efficient analytical applications.

Github: https://github.com/ryanrhymes/owl

Liang wang small

Liang W. added photos to project Owl - An OCaml Numerical Library

Medium db2ecfbb 43c5 458b b362 c0e908afd357

Owl - An OCaml Numerical Library

Owl is an OCaml library for scientific computing. It enables academic researchers to fast prototype machine learning algorithms and construct deep neural networks with very concise code. It also facilitates industry programmers to develop robust analytical applications using functional language at a large scale.

Currently, Owl supports N-dimensional arrays, both dense and sparse matrix operations, linear algebra, regressions, fast Fourier transforms, and many advanced mathematical and statistical functions (such as Markov chain Monte Carlo methods). Recently, Owl has also implemented algorithmic differentiation, on top of which we have been building a powerful deep neural network module. With its advanced underlying distributed computation engine, Owl is able to support distributed computing to take advantage of the computation power in a computer cluster.

Owl is under active development. It aims to provide a comprehensive set of advanced numerical functions to enable fast development of robust and efficient analytical applications.

Github: https://github.com/ryanrhymes/owl

Medium liang wang small

Liang W. created project Owl - An OCaml Numerical Library

Medium db2ecfbb 43c5 458b b362 c0e908afd357

Owl - An OCaml Numerical Library

Owl is an OCaml library for scientific computing. It enables academic researchers to fast prototype machine learning algorithms and construct deep neural networks with very concise code. It also facilitates industry programmers to develop robust analytical applications using functional language at a large scale.

Currently, Owl supports N-dimensional arrays, both dense and sparse matrix operations, linear algebra, regressions, fast Fourier transforms, and many advanced mathematical and statistical functions (such as Markov chain Monte Carlo methods). Recently, Owl has also implemented algorithmic differentiation, on top of which we have been building a powerful deep neural network module. With its advanced underlying distributed computation engine, Owl is able to support distributed computing to take advantage of the computation power in a computer cluster.

Owl is under active development. It aims to provide a comprehensive set of advanced numerical functions to enable fast development of robust and efficient analytical applications.

Github: https://github.com/ryanrhymes/owl

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Silviu-Tudor S. created project ASTRO

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ASTRO

Robots are becoming an increasingly ubiquitous part of our society, providing help with a wide array of tasks such as cleaning our homes, delivering room service or guiding us to our departure gate at the airport. The adoption of robot solutions is becoming a sensible choice, if not a priority, for an increasing number of businesses. ASTRO is a versatile robot designed to improve productivity and safety in the workplace. Its name is an acronym for Assistance, Safety and Telepresence Robot Officemate.

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Justin L. created project Edge of Home

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Edge of Home

This experience, developed for Kennedy Space Center, allows you to explore the International Space Station (ISS) like only astronauts have. See what it is like to take a spacewalk with a NASA astronaut guide. Learn about each part of the ISS, and the part they will play in the future of space exploration.

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Justin L. added photos to project Shapesong

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Shapesong

Demoed at GDC 2017, OrlandoiX 2016, Austin Game Conference 2016, IDF16, and SIGGRAPH 2016.

Winner of Best of Show at the Intel Game Developer Showcase, Austin Game Conference 2016.

Winner of Most Innovative Use of AR/VR, OrlandoiX 2016.

Shapesong is a concept we are developing at Chronosapien that lets people jump into VR and start playing music as easily as banging on a drum. Our goal is to give everyone the experience of playing and creating music, playing with others (even themselves), and sharing those creations and performances with everyone—both inside and outside of VR. By creating your own instrument, playing music can be as simple or as complex as you want, and can be tailored exactly to your ability and style. With VR we can finally let go of the physical limitations of traditional musical instruments and create ones that are completely unique and made purely of the imagination.

Shapesong is being developed specifically for room-scale VR platforms. We are currently prototyping the app and will show more media and information as we progress.

Update 1: SIGGRAPH and IDF build

We've been working on a proof of concept build of Shapesong to demo at both SIGGRAPH and IDF. In this version, we have a forest garden area with some different instruments and interactive visuals for people to explore and play with. There are also a number of sound cartridges that can be used to load new sounds onto instruments. At end of the garden, we have a performance area that brings all of the instruments together so that players can create a song by recording and looping themselves, as well as playing with another character.

With this version we plan to collect feedback from those at the trade shows and use it to define our next build of Shapesong.

Update 2: Austin Game Conference, Teaser Trailer

We just got back from Austin Game Conference, where Shapesong won "Best in Show" at the Intel Game Developer Showcase. Shapesong was well-received, and we can't wait to start sharing new features as they're developed. Our first teaser trailer for Shapesong is up on Youtube (link below) as well, so check it out!

Update 3: GDC 2017

A brand new version of Shapesong was playable for the first time at GDC 2017 this past week, this time in Mixed Reality! Above the GDC show floor at the Intel Sky Bridge, Shapesong was playable in a fully green booth—allowing us to capture the user in mixed reality. Everyone who ran through Shapesong (except for one, sorry Eileen!) was able to walk away with a flash drive containing their experience in Shapesong, as a mixed reality recording. We uploaded a new video of Shapesong (Intel Developer Zone Update for March 2017) and will be uploading some mixed reality videos soon.

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