#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 – I will be speaking at @devdotnext #devdotnext2020 this March in Colorado.

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Hi!

In a couple of weeks, I’ll be visiting one of the biggest events in Broomfield, Colorado: @devdotnext.

DevDotNext

DevDotNext hosts 150+ 75-minutes Presentations, 4 Keynotes/Panels, and 11 All-day Pre-Conference Workshops.

Topics covered include:

  • Languages
  • Design and Architecture Cloud
  • Server-Side
  • Frontend
  • DevOps
  • Microservices
  • Machine Learning
  • Testing
  • Being agile
  • Leadership
  • And more

I’ll be sharing some experiences and insights around Machine Learning, Computer Vision and IoT. Here are my session details.

How a PoC at home can scale to Enterprise Level using Custom Vision APIs (v2!)

It all started with a DIY project to use Computer Vision for security cameras at home. A custom Machine Learning model is the core component used to analyze pictures to detect people, animals and more in a house environment. The AI processing is performed at the edge, in dedicated hardware and the collected information is stored in the cloud.

The same idea can be applied to several CCTV scenarios, like parking lots, train stations, malls and more. However, moving this into enterprise scale brings a set of challenges, which are going to be described and explained in this session.

In this new version of the session, we will start from scratch and create a complete “Parking Garage Open Space Tracker” solution with live devices and live cars (small ones, of course)

Registration and event details

Hurry up, regular registration ends soon.
Register at https://www.devdotnext.com/register

Happy coding!

Greetings

El Bruno

#MachineLearning – Let's start 2020 with a free Ebook: Pattern Recognition and Machine Learning

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pattern recognition and machine learning christopher bishop

Hi !

I’ve posted this one some time ago, however it’s still a free and VERY USEFUL one !

Christopher Bishop, Technical Fellow and Laboratory Director In Microsoft Research Cambridge, UK, gives us the chance to download for free his eBook about Pattern Recognition and Machine Learning. With more than 700 pages of a highly recommended reading

Pattern Recognition and Machine Learning

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. This is the first machine learning textbook to include a comprehensive coverage of recent developments such as probabilistic graphical models and deterministic inference methods, and to emphasize a modern Bayesian perspective. It is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. This hard cover book has 738 pages in full colour, and there are 431 graded exercises (with solutions available below). Extensive support is provided for course instructors.

Download: https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/

Happy Coding !

Greetings @ Toronto

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

#Azure – Azure Open DataSets, an amazing friend for Azure #ML Studio (Preview)

Hi!

Time for a very interesting feature part of the Azure family: Azure Open Datasets. OK, when you read the name, you probably get 95% of the idea, however, let’s dig into the official definition (see references).

Azure Open Datasets are curated public datasets that you can use to add scenario-specific features to machine learning solutions for more accurate models. Open Datasets are in the cloud on Microsoft Azure and are integrated into Azure Machine Learning and readily available to Azure Databricks and Machine Learning Studio (classic). You can also access the datasets through APIs and use them in other products, such as Power BI and Azure Data Factory.

Datasets include public-domain data for weather, census, holidays, public safety, and location that help you train machine learning models and enrich predictive solutions. You can also share your public datasets on Azure Open Datasets.

This per-se is amazing, however this feature became useful when you start to work with the new amazing Azure Machine Learning Studio (Preview). Now in the [Assets / Datasets] section we can use:

  • Datasets from local files
  • Datasets from DataStore
  • Datasets from WebFiles
  • Datasets from the Open DataSet repository

And the last one is awesome because we can work with sample and free data like

All the datasets in the repository are optimized to be used in Machine Learning workflows. And, we have the chance to requests datasets or to submit and contribute with our own data. So Cool!

Happy coding!

Greetings @ Toronto

El Bruno

References

#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 – Building an Anomaly Detector System with a few or no lines of code at @MSFTReactor

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Hi !

Microsoft opened a brand new Microsoft Reactor in Toronto, and I’m lucky enough to host a AI session about Anomaly Detection. Below are the details

Detecting anomalies is a common scenario which can be applied to dozens of industries. From the analysis of power consumption, medical data, or even analysis of personal information, anomalies can be detected based on historical data.

During this workshop, Bruno will guide attendees to code a complete system that will detect anomalies: you will train a model based on historical data, and later use the same model with new data to identify anomalies. At the end of the workshop, attendees will review a new set of options to create an Anomaly Detection System without a single line of code!

Please bring a laptop or other personal device to participate in this hands-on workshop.

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

Happy Coding!

Greetings @ Burlington

El Bruno