#Event – Materiales utilizados en la sesión [Getting Started with Machine Learning.Net & Windows Machine Learning] con el grupo de usuarios GLUG

Buenas!

Es momento de otro post para compartir los materiales utilizados durante la sesión con el grupo de usuarios GLUGnet User Group for .NET, Web, Mobile, Database. Especial agradecimiento para Joe Kunk (@JoeKunk) y a los asistentes al webcast vía Google Hangouts. Por cierto, la sesión fue con los materiales actualizados a la version 0.10 de ML.NetÑ Windows ML and Machine Learning.Net.

Como siempre, aquí están las slides.

Source Code GitHub https://github.com/elbruno/events/tree/master/2019%2002%2021%20GLUG%20NetUG%20MLNet

Y algunos links que comenté durante la sesión:

Resources

Happy Coding!

Saludos @ Toronto

El Bruno

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#Event – Resources for the session [Getting Started with Machine Learning.Net & Windows Machine Learning] on the GLUG .Net User Group

Hi!

Another post-event post, this time with a big thanks to Joe Kunk (@JoeKunk) and to all the members of the GLUGnet User Group for .NET, Web, Mobile, Database. We had an amazing time, via Google Hangouts in the session about Windows ML and Machine Learning.Net.

As usual, now it’s time to share slides, code and more.

Source Code GitHub https://github.com/elbruno/events/tree/master/2019%2002%2021%20GLUG%20NetUG%20MLNet

And some Machine Learning.Net resources:

Resources

Greetings @ Toronto

El Bruno

#Event – Resources for the session [Getting Started with Machine Learning.Net & Windows Machine Learning] on the Tech Valley .NET UG @TUVG

Hi!

Another post-event post, this time with a big thanks to Chris Miller (@anotherlab) and to all the members of the Tech Valley .NET User Group. We had an amazing time, via Skype in the session about Windows ML and Machine Learning.Net.

As usual, now it’s time to share slides, code and more.

Source Code GitHub https://github.com/elbruno/events/tree/master/2018%2012%2018%20TechValley%20NetUG%20MLNet

And some Machine Learning.Net resources:

Resources

Greetings @ Toronto

El Bruno

#Event – Resources for the session [Getting Started with Machine Learning.Net & Windows Machine Learning] on the Mississauga .NET User Group

Hi!

Another post-event post, this time with a big thanks to Obi (@ObiOberoi) and to all the members of the Mississauga .NET User Group. We had an amazing time a couple of days ago in the session about Windows ML and Machine Learning.Net.

As usual, now it’s time to share slides, code and more.

Source Code GitHub https://github.com/elbruno/events/tree/master/2018%2011%2022%20Mississauga%20MLNet

And some Machine Learning.Net resources:

Resources

Greetings @ Toronto

El Bruno

#Event – Materiales utilizados en la sesión [Getting Started with Machine Learning.Net & Windows Machine Learning] con el grupo de Mississauga .NET User Group

Buenas!

Momento de agradecer al amigo Obi (@ObiOberoi) y a los miembros del grupo Mississauga .NET User Group por el apasionante rato que pasamos hace unos dias hablando de Windows ML y de Machine Learning.Net.

Como es habitual, ahora es el momento de compartir slides y Source Code de los ejemplos comentados ayer.

Muchas gracias por las preguntas y por el Feedback, todos 5 estrellas!

Source Code GitHub https://github.com/elbruno/events/tree/master/2018%2011%2022%20Mississauga%20MLNet

A continuación algunos recursos que comente en durante la sesión:

Resources

Saludos @ Toronto

El Bruno

#WindowsML – Create Native AI apps for #Hololens #Windows10

Hi!

A couple of weeks ago I wrote about a new feature in Windows 10 to be able to use ML models natively in W10 Apps. For me, that would mean a breakthrough in the entire Windows 10 Device ecosystem. In example, as we already know, the new version of Hololens incorporates a chip specially dedicated to AI tasks (DNN specifically),
so I assumed that in Hololens V2 we could start using Windows ML using the device capabilities.

What I did not expect is that in the Preview version of Windows 10 RS4 for Hololens we already had access to that feature. I have not had time to update my Hololens to RS4, however, a couple of community experts have shared more than interesting experiences.

For example, let’s see this video by Rene Schulte, where he shows an App that performs a real time image classification using the SqueezeNet ONNX model

Mike Taulty also offers a series of posts where he does an excellent research job in Windows ML and Hololens. He started on how to create a model using Azure Custom Vision, export it to CoreML and then create an ONNX model.

Important: currently ONNX is the type of models supported by Windows ML. It is possible to export CoreML models, TensorFlow, CNTK and other frameworks to ONNX.

From here, Mike comments on how to work with UWP and C # with the exported model. In subsequent posts, Mike also explains how, thanks to Hololens RS4 Preview, these capabilities can also be used in applications for Hololens (see references)

As well, If you dare to upgrade your Hololens to RS4 Preview, it is a very interesting moment to start thinking about scenarios where you can combine WinML and Hololens!

Happy Coding!

Greetings @ Toronto

El Bruno

References

#WindowsML – Ya se puede crear apps con AI nativo en #Hololens #Windows10

 

Buenas!

Hace un par de semanas comente que la nueva capacidad en Windows 10 de poder utilizar modelos de ML de forma nativa en W10 Apps significaría un gran avance en todo el ecosistema de Devices de Windows 10. Como ya sabemos que la nueva version de Hololens incorporar un chip especialmente dedicado a tareas de AI (DNN específicamente), pues era de suponer que en Hololens V2 podríamos comenzar a utilizar Windows ML.

Lo que no esperaba es que en la version Preview de Windows 10 RS4 para Hololens ya tuviésemos acceso a esa capacidad. No he tenido tiempo de dedicarle a mis Hololens para actualizarlas a RS4, sin embargo, grandes referencias han compartido experiencias más que interesantes.

Por ejemplo, veamos este video de Rene Schulte, donde muestra una App que en tiempo real realiza una clasificación de imágenes utilizando el modelo ONNX SqueezeNet

Mike Taulty también ofrece una serie de posts donde realiza un trabajo excelente al respecto. En primer lugar, comenta como crear un modelo utilizando Azure Custom Vision, exportar el mismo a CoreML y luego crear un modelo ONNX.

Importante: actualmente ONNX es el tipo de modelos soportados por Windows ML. Es posible exportar modelos de CoreML, TensorFlow, CNTK y otros frameworks a ONNX.

A partir de aquí, Mike comenta como trabajar con UWP y C# con el modelo exportado. En siguientes posts, Mike también explica cómo, gracias a Hololens RS4 Preview, también se pueden utilizar estas capacidades en aplicaciones para Hololens (ver referencias)

Pues bien, si te animas a poner una version Preview en Hololens, es un momento muy interesante para comenzar a pensar en escenarios donde combinar WinML y Hololens!

Happy Coding!

Saludos @ Toronto

El Bruno

References

#Event – Materials used in the event [Introduction to Microsoft AI] with a bit of #WinML

Hi!

As always, it’s a pleasure to talk about Microsoft’s Artificial Intelligence platform. We had full house, and also the visit of Valentino and Martina.

On this occasion there were many attendees with a profile of Data Scientist, so the questions were more than interesting. The support and collaboration of Asmita were fundamental to answer all the questions about Azure Machine Learning, CNTK, Windows Machine Learning and other topics.

Now the classics, slides

GitHub source code link

Happy Coding!

Greetings @ Toronto

El Bruno

#Event – Materiales utilizados en el evento [Introducción a Microsoft AI] con un poco de #WinML

Buenas!

Como siempre ha un placer hablar sobre la plataforma de Inteligencia Artificial de Microsoft. Tuvimos full house, y ademas la visita de Valentino y Martina.

En esta ocasión hubo muchos asistentes con un perfil de Data Scientist con lo que las preguntas fueron mas que interesantes. El soporte y la colaboración de Asmita fueron fundamentales para poder responder todas las preguntas sobre Azure Machine Learning, CNTK, Windows Machine Learning y otros temas.

Ahora los clásicos, slides

Y source code en GitHub link

Happy Coding!

Saludos @ Toronto

El Bruno

#WinML – #GamingML created for #Windows10 gamers (#Unity3D and #Hololens V2 included!)

Hi!

First of all, I’ll start by saying that I finally found a hashtag for Windows Machine Learning posts: [#WinML]. A small summary of WindowsML (actually from the part that interests me)

The WinML API allows us (Windows 10 developers) to use Machine Learning trained models and make inferences with them on a wide variety of hardware (CPU, GPU, VPU). An ML programmer may choose a Framework, such as CNTK, Caffe2 or Tensorflow, and with it he could build and train a ML model.

That model would then be converted to the Open Neural Network Exchange (ONNX) a format co-developed between Microsoft, Facebook and Amazon. As of this moment, a Windows 10 application can use this ML model internally as part of the App.

This in itself is great, however, there is a new layer more specifically created for games: DirectML. DirectML is built on top of pf Direct3D and represents a special layer for Gaming scenarios that provides Hardware Acceleration GPU for WindowsML operations.

In the references I left the article where all the technical details of DirectML are covered.

WinMLArchitecture

Another interesting point of the news is the announcement of support for Unity ML-Agents. Native WindowsML / DirectML integration will be available for Windows 10 games created with Unity 3D.

Personally, I did not know the Unity Machine Learning Agents. After reading a bit about them, it is clear to me that in the near future, the games and Apps created with Unity3D will be completely different from what we know today.

Bonus

 

It’s time to start connecting dots and entering the area of ​​speculation. A while ago it became public that the new version of Hololens will have a new chip specifically dedicated to operations of [Deep Neural Networks] (see references). From a generic point of view, Hololens is just another type of Windows 10 device.

If we assume that Hololens V2 uses a version of Windows 10 that supports out of the box the capabilities of Windows ML, we already have a Mixed Reality Headset with impressive capabilities where Apps can use ML natively (the chip is already there!).

From the point of view of a C # developer, this is great!

Happy Coding!

Greetings @ Burlington

El Bruno

References