#Event – Machine Learning.Net y AutoML, esta vez en Español !

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Buenas !

Seguimos en modo StayAtHome, y una forma excelente de conectar con las comunidades, es participando en eventos ya sea como Speaker o como Attendee.

Esta vez tengo la oportunidad de hablar en NetCoreConf:

NetCoreConf 2020

Lo último en tecnologías Microsoft y mucho más con los mejores expertos. Donde podrás aprender, compartir y hacer networking. Asistiendo a diversas Conferencias y Workshops. Hablaremos sobre NetCore, Azure, Xamarin, IA, Big Data. ¿A que estas esperando?

NetCoreConf 2020 realizará el primer evento virtual a nivel global dedicado exclusivamente al sector del desarrollo y consultoría que busca descubrir y dar a conocer las nuevas tecnologías de vanguardia y crear vínculos estratégicos que generen sinergias conjuntas entre los profesionales del sector, empresas e instituciones.

NetCoreConf 2020

Mas información NetCoreConf Virtual 2020

La agenda es impresionante, y yo hablaré de uno de los productos más interesantes que Microsoft ha presentado en los últimos años: Machine Learning.Net. En mi sesión comentaré un poco la historia y algunos ejemplos del producto, y además un poco de una herramienta muy interesante para los no programadores: AutoML.

Finalmente, agradecer al gran equipo que esta detrás de este evento:

Happy coding!

Greetings

El Bruno

#Event – Materials and resources used during TechDay Conf, #techdayconf #techdayconf2020

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Hi !

I shared some knowledge around Machine Learning.Net last Saturday during the TechDays virtual conference. And, as usual, it’s time to share the resources I used in this session.

Full event

Slides

Resources

Happy coding!

Greetings

El Bruno

#Event – TechDay Conf, virtual sessions in English and French #techdayconf #techdayconf2020

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Hi !

I will participate this Saturday on a full day of technical sessions around technologies like Machine Learning, Azure, Entity Framework, .Net Core and more! The agenda is amazing, take a look:

Sessions in French

  • 10h – 10h20: Denis Voituron – Evitez Entity Framework. Faites du Mappage Relationnel Objets
  • 10h20 – 10h40: Charles-Henri Sauget – Data Lake Architecture dans 15 Min
  • 10h40 – 10h55: Anouar Ben Zahra – Cognitive Services dans 15 minutes
  • 11h00 – 11h20: Cédric Derue – Utiliser Azure Functions pour votre stratégie cloud hybride ou multi-cloud

Sessions in English

  • 11h20 – 11h40: Alibek Jakupov – Knowledge Extraction from Unstructured Data using Azure ML services and Power BI
  • 11h40- 12h15: Bruno Capuano – Getting Started with Machine Learning.Net and Windows Machine Learning
  • 12h15- 12h40: Khaled Tounsi – Feature management with Azure App Configuration
  • 12h40 – 13h05: Damien Delaire – Building one UWP app for Xbox and Windows XAML/C#, 20 minutes Chrono to build Dailymotion (light)!
  • 13h05 – 13h20: Hamida REBAI – Build and deploy .NET Core application in Azure

And important, the time mentioned is in Canada – Quebec, for European we need to add 6 hours so 10h is 16h

To get access to the live streaming details and more, please register using this link: https://techdayconf.eventbrite.fr

Happy coding!

Greetings

El Bruno

#Event – Resources used during Getting started with #MachineLearning.net with @TheDataGeeks

Hi!

It was a placer to share some amazing and early time with the Data Platform Geeks in a webinar about Machine Learning.Net.

Slides

Source Code

https://github.com/elbruno/events/tree/master/2020%2001%2021%20DPG%20MLNet

Resources

Event information

https://www.linkedin.com/feed/update/urn:li:activity:6625330471557529600

https://www.linkedin.com/feed/update/urn:li:activity:6625330471557529600

Happy Coding!

Greetings @ Burlington

El Bruno

#Event – Resources used during the #MachineLearning Galore at @MississaugaNetU

Hi!

It was a placer to share some amazing time with the Mississauga .Net User Group last night in my last session of the decade. It was a full night focused on Artificial Intelligence and Machine Learning, and as usual is time to share the resources used in the session.

Slides

Source Code

https://github.com/elbruno/events/tree/master/2019%2009%2005%20Global%20AI%20Night

Resources

Event information

Happy Coding!

Greetings @ Burlington

El Bruno

#VS2019 – Let’s do some image classification with #MLNET Model Builder! (AKA, let’s create an image classifier model without a line of code)

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Hi!

I’m getting ready for my last event of the year, and I just realize that in the latest update of Model Builder, we have the chance to build our own Image Classifier scenario. Let’s start with the official Model Builder definition (see references):

ML.NET Model Builder provides an easy to understand visual interface to build, train, and deploy custom machine learning models. Prior machine learning expertise is not required. Model Builder supports AutoML, which automatically explores different machine learning algorithms and settings to help you find the one that best suits your scenario.

Working with images was supported for a while in Machine Learning.Net. In the Machine Learning .Net Samples, we have sample scenarios like

Image Classification Model Training – Preferred API (Based on native TensorFlow transfer learning)

In this sample app you create your own custom image classifier model by natively training a TensorFlow model from ML.NET API with your own images.

We even have an amazing tutorial, to create our own image classification model from zero

Tutorial: Generate an ML.NET image classification model from a pre-trained TensorFlow model

Learn how to transfer the knowledge from an existing TensorFlow model into a new ML.NET image classification model. The TensorFlow model was trained to classify images into a thousand categories. The ML.NET model makes use of part of the TensorFlow model in its pipeline to train a model to classify images into 3 categories.

Training an Image Classification model from scratch requires setting millions of parameters, a ton of labeled training data and a vast amount of compute resources (hundreds of GPU hours). While not as effective as training a custom model from scratch, transfer learning allows you to shortcut this process by working with thousands of images vs. millions of labeled images and build a customized model fairly quickly (within an hour on a machine without a GPU). This tutorial scales that process down even further, using only a dozen training images.

And now, I found that Model Builder, also supports an Image Classification Scenario.

It follows the Model Builder standard workflow, starting with the selection of the scenario:

model builder select scenario

And then selecting a folder with the Images.

model builder images for training

Important: Model Builder expects image data to be JPG or PNG files organized in folders that correspond to the categories of the classification.

To load images into Model Builder, provide the path to a single top-level directory:

  •     This top-level directory contains one subfolder for each of the categories to predict.
  •     Each subfolder contains the image files belonging to its category.

Once the folder is selected, we can see a preview of the images and labels loaded from the folder.

model builder folder selected image preview

For more information about how to organize images for this scenario, refer to Load training data into Model Builder.

And now we start the training process. This may take a while, depending on your hardware. I’m using the sample set of drawings that we used on the InsiderDev Tour, for Custom Vision. These are 24 drawings images, with 3 labels, and in a PC with a I7, 32GB of Ram and an SSD, the training process took a little longer than 2 minutes.

model builder train images complete

Once the training is complete, we have a decent accuracy in our model, so it’s time to test. Before Model Builder last step, we have the chance to test the model with some test images.

Using one of the images that I created at Ignite in Orlando, the trained model get’s a human with a 99% of accuracy.

model builder model trained test image

And, the final step is to add the generated model and code to our project. I’ll write about how to use this generated code on the near future.

model builder code generated

Happy Coding!

Greetings @ Burlington

El Bruno

References

#Event – #GlobalAIBootcamp, thanks to @AvanadeInc and @MicrosoftCanada, here are some resources, feedback and more !

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Hi !

The Global AI Bootcamp 2019 was a huge success, just take a look at this photo during the keynote

And remember, this was the weather during the day, cold and snow. Even so, we had ~200 attendees at the Microsoft Offices in Mississauga.

Speakers and Volunteers

We had an amazing set of volunteers and speakers, and I’m afraid I’m missing some names, so here it goes a photo of both:

Sponsors

Our 2 main sponsors were Avanade and Microsoft, thanks !

And, as we did in previous events, instead of having swag, we prefer to donate to a charity for every interaction we had at our booth!

Agenda

This year Agenda was amazing, and it’s tricky to summarize it in a single image

Take a look at this post: https://elbruno.com/2019/12/10/event-globalaibootcamp-2019-we-got-an-amazing-agenda-take-a-look/

Resources and Feedback

I’ll leave some feedback and general tweets at the end of this post.

As a bonus, here it goes my slides around Auto ML with Machine Learning.Net.

Here is the official Keynote:

Tweets

And tweets, more tweets

Happy Coding !

Greetings @ Burlington

El Bruno

#Event – Resources used during my session “Building an Anomaly Detector System with a few or no lines of code” @MsftReactor

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Hi!

My 1st session at Microsoft Reactor and it was amazing. We had engaging conversations around Machine Learning and Anomaly Detection, and as I promised here are the resources used.

Building an Anomaly Detector System with a few or no lines of code

More Information https://www.microsoftevents.com/profile/form/index.cfm?PKformID=0x8330114abcd

Slides

Source Code

https://github.com/elbruno/events/tree/master/2019%2011%2021%20Anomaly%20Detections%20Reactor

Reference Links

General

Machine Learning.Net

Cognitive Services Anomaly Detector

Azure Machine Learning

Happy coding!

Greetings @ Toronto

El Bruno

#Event – 2×1 on #AI: Anomaly Detection at Microsoft Reactor and ML.Net @MississaugaNetU

Photo by Sachin C Nair on Pexels.com
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Hi!

Quick post to remember about a couple of events where I’ll participate.

1st, this Thursday at Microsoft Reactor in Toronto.

Building an Anomaly Detector System with a few or no lines of code

November 21, 2019 | 5:00PM – 7:00PM

Reactor Toronto
MaRS Centre, Heritage Building 101 College Street, Suite 120
Toronto Ontario M5G-1L7
Canada

More Information https://www.microsoftevents.com/profile/form/index.cfm?PKformID=0x8330114abcd

And, I’m happy to go back with my friends from the Mississauga DotNet User Group.

Machine Learning Galore!

Thursday, December 19, 2019
6:00 PM to 8:00 PM

More Information https://www.meetup.com/MississaugaNETUG/events/266518936/

Happy Coding!

Greetings @ Toronto

El Bruno

#Event – Resources used during the #GlobalAINight at @MarsDD

Hi!

It was a placer to share some amazing time with the Metro Toronto .Net User Group. Last night was also a special one, we hosted the event at the amazing @MarsDD it was great to have a huge group interested in Artificial Intelligence.

As usual, it’s time to share the resources of the event

Official Resources https://aka.ms/AA60hn1

This includes Workshops like

  • Creating applications that can see, hear, speak or understand – using Microsoft Cognitive Services
  • Learn how to train high accuracy machine learning models using automated machine learning
  • Crash course on building and accelerating deep learning solutions
  • And more.

It also includes a set of materials around Automated Machine Learning (AutoML).

And of course, my materials.

Slides

Source Code: https://github.com/elbruno/events/tree/master/2019%2009%2005%20Global%20AI%20Night

Happy Coding!

Greetings @ Toronto

El Bruno

Resources

Tweets