#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 – Resources used on the [#ArtificialIntelligence and #MachineLearning in #Azure] event

04

Hi!

Let me start with a big Thanks to my friends on [The Azure Group (Canada’s Azure User Community] for all the work and amazing time in my session [Artificial Intelligence and Machine Learning in Azure].

As usual, now it’s the share resources time. This one will be slides and tons of links, the source code was to basic to even push to GitHub

And some interesting links

Windows 10 and YOLOV2 for Object Detection Series

Happy coding !

Greetings @ Burlington

El Bruno

 

 

#Event – Materiales utilizados en la sesión [#ArtificialIntelligence and #MachineLearning in #Azure]

04

Buenas!

Gracias a los amigos de [The Azure Group (Canada’s Azure User Community] por el excelente rato hace un par de días en la sesión [Artificial Intelligence and Machine Learning in Azure].

Como siempre, ahora es el momento de compartir las slides y materiales utilizados en la sesión

Y esta vez en lugar de código, pues una lista larga de recursos

Windows 10 and YOLOV2 for Object Detection Series

Happy coding !

Saludos @ Burlington

El Bruno

 

 

#Podcast – NTN 40 – Introduction to Machine Learning (lucky for us, JavaScript free)

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Hello!

Episode dedicated to Machine Learning. I am very lucky to have 2 experts on the subject, Pablo Alvarez Doval (@PabloDoval) and Rodrigo Cabello (@mrcabellom).

As always we had a prepared agenda, however, along the way we deviated from the agenda, and things became more real. We cover the definition of Machine Learning, we also talk about some languages ​​and tools that we have available for ML, and about the Frameworks that we can use today. AzureML, CNTK, Tensor Flow, Python, R and real experiences of ML projects. I like to learn / listen to real experiences.

Special mention to the reference of Pablo on the “you do not need to be a mathematician to work in ML”, and the explanation of Rodrigo on the reason for the popularity of Python in the ML community.

At the end of the episode we have the latest technology thanks to Sergio Mabres. I hope you enjoy, and apologize for the mistakes (if there were any).

 

Greetings @ Burlington

El Bruno

References

#Podcast – NTN 40 – Introducción a Machine Learning (todavía sin JavaScript)

giphy.gif

Hola!

Episodio dedicado a la Machine Learning. Tengo la gran suerte de contar con 2 expertos en el tema, Pablo Alvarez Doval (@PabloDoval) y Rodrigo Cabello (@mrcabellom).

Como siempre teníamos una agenda preparada, sin embargo, por el camino nos desviamos de la agenda. Eso sí, cubrimos la definición de Machine Learning, también hablamos sobre los lenguajes y herramientas que tenemos disponibles, y sobre los Frameworks que podemos utilizar hoy. AzureML, CNTK, Tensor Flow, Python, R y experiencias reales de proyectos de ML. Como siempre, yo intento aprender en base a experiencias reales.

Especial mención a la referencia de Pablo sobre la “no necesidad de ser un matemático para trabajar en ML”, y la explicación de Rodrigo sobre el porqué de la popularidad de Python en la comunidad de ML.

Al final del episodio tenemos las novedades de tecnología gracias a Sergio Mabres. Espero que lo disfruten, y que disculpen los errores (si es que hubo alguno).

Saludos @ Burlington

El Bruno

References