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.
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.
it’s time to share the resources of the event
recursos para comenzar a aprender Machine Learning. Sin embargo, suele ser
complicado elegir uno que realmente se adapte a nuestro perfil, y que nos
permita aprender de forma coherente y concisa los principios de Machine
Learning. Si trabajas con tecnologías Microsoft o eres un programador .Net,
este curso es para ti.
curso veremos los conceptos principales que explican el estado actual de
Machine Learning; y aprenderemos utilizando ML.NET (Machine Learning.Net ).
ML.Net es un conjunto de herramientas que ha alcanzado de forma oficial su
versión Release y que será la base del aprendizaje de Machine Learning. Veremos
escenarios para aprendizaje supervisado y no supervisado, escenarios de
análisis de sentimientos de texto, escenarios de integración con otras
tecnologías de ML, como ONNX o TensorFlow, y mucho más.
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.
The tool is on Preview, but it’s still an amazing one to play around with ML. So I decided to give it a try with my small data set of kids, the one I use on the Machine Learning.Net demos.
The structure of my CSV file is very simple with just 3 columns: Age, Gender and Label.
However the first time I run the scenario I found the following error.
Inferring Columns ...
Creating Data loader ...
Loading data ...
Exploring multiple ML algorithms and settings to find you the best model for ML task: regression
For further learning check: https://aka.ms/mlnet-cli
| Trainer RSquared Absolute-loss Squared-loss RMS-loss Duration #Iteration |
[Source=AutoML, Kind=Trace] Channel started
Exception occured while exploring pipelines:
Provided label column 'Label' was of type String, but only type Single is allowed.
System.ArgumentException: Provided label column 'Label' was of type String, but only type Single is allowed.
at Microsoft.ML.CLI.Program.<>c__DisplayClass1_0.<Main>b__0(NewCommandSettings options)
Please see the log file for more info.
Which makes a lot of sense, my Label column is a String and the Model Builder expects a Single data type. So, I updated my data file replacing the labels with numbers and I was ready for a 2nd test.
This time the training process started fine, however I noticed that using just a small training dataset didn’t trigger any comparing between different algorithms. So I created a much bigger training dataset, and now I got the training process up and running.
At the end the results are the ones below. And it’s very interesting. I do most of my demos using a MultiClass SDCA trainer and AutoML suggest me to use a LightGBM trainer. This will be part of my Machine Learning.Net speech for sure in the future.