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Community centered care data analysis, times of Covid-19

For the past few weeks, the global health emergency caused by Covid-19 has put many questions in my thoughts, from general to more specific in many dimensions. We all know that we should prepare better in the future and this morning reviewing the concept of community centered care “ At the Epicenter of the Covid-19 Pandemic and Humanitarian Crises in Italy: Changing Perspectives on Preparation and Mitigation ,(2020), Nacoti, Ciocca, ... ” added by the hospital in Bergamo Italy, sparked my interest... how to be prepared, how potentially implement and be smart into the distribution, finally using data from Korea( KCDC  and DS4C ), I started analyzing looking for responses. From the data analysis perspective if we can understand the entities and important variables geographically distributed in a region, we can analyze the activity of this approach at least in theory dimension. Also with an analysis of infection-cases we can explore the more affected regions and maybe create a distr
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Are you interested in creating a TensorFlow SIG?

If you are interested in contributing with TensorFlow, a very interesting option is the proposal of SIG. TensorFlow hosts Special Interest Groups (SIGs) to focus collaboration on particular areas. What is a TensorFlow SIG? SIGs do their work in public. The ideal scope for a SIG meets a well-defined domain, where the majority of participation is from the community. Additionally, there should be sufficient evidence that there are community members willing to engage and contribute should the interest group be established. More information here,  https://www.tensorflow.org/community/sig_playbook If you have ideas, and you want to participate with me into this initiative, here I have a open communication channel to start the analysis of the proposal: https://tensorflowexperiences.nicolasbortolotti.com/main/sig Esta obra está bajo una Licencia Creative Commons Atribución-CompartirIgual 4.0 Internacional .
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Cloud Platform & Communities summer experiences in Switzerland & Italy

A couple of weeks ago with the European developers communities team we started activities with some new communities specialized in Machine Learning & Cloud Platform.  We also participated in some activities and as part of this initiative, we visited two communities with a practical program proposal  based on Cloud Technology. Cloud Study Jam . We shared a very nice activity with SOAI chapter in Zurich . Here we present the basic elements of the Cloud Study Jam proposal and we also explored a workshop with the introduction to Cloud ML engine. Cloud Study Jam Overview: *a core element of Cloud Study Jam format is the utilization of qwiklabs platform to provide tech tutorial & online labs. Cloud Study Jam, workshop "Cloud ML Engine: Qwik Start": In addition to that, we shared another very nice activity with Machine Learning Milan . Similar approach with Cloud Study Jam Overview and then we provided a workshop related to AutoML Vision. Cloud Study Ja
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An service option to interact with Arlo cameras

Interacting with the Arlo system is very attractive for who acquired the hardware and enjoy all the benefits of these equipments. Which by the way are very good, flexible and full of good properties. To interact with this system with a developer's perspective I found two interesting python-based projects. Python-arlo:  https://github.com/tchellomello/python-arlo Arlo:  https://github.com/jeffreydwalter/arlo Both propose different features but Python-arlo has a good documentation and structure of its API. The objective for this integration is to support applications from Google Assistant , for this reason I  integrated the service into an App Engine flex env project. I included flask as an interaction framework and with this I have the option to use a service interface. This would be our ideal architectural map: We looking for continue with the application initiated from: Hey Google ... Where is my dog? * Of course we will have to implement
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Google Assistant, simpleness of interaction to call a webhook

The creation of an application using Google Assistant is very simple and represent a big impact to interact with users in diverse environments and particularly in home Hey Google ...Where is my dog? If we plan to connect a security camera system, the challenge of finding my pet in an automated way is more feasible, then I will give more details of how I intend to do it. Now we are going to prepare the functionality of the assistant’s requests. To create an agent and interact with Google Assistant , we will use DialogFlow . Let's see the main elements to create an agent, and using this agent call a webhook with some functionality. * Part of the objective will be to try to prepare the logic of interaction with a home security system * Arlo System From the DialogFlow console, we will create a new agent: We can customize different support languages, for this case I will use English to facilitate the testing of the agent. [You can include Spanish and also go in
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Following Messi with TensorFlow and Object Detection

In previous publications we were using TensorFlow in combination with the Object Detection model, but always making use of the traditional pre-established datasets [example COCO database ]. This time our challenge should take us another level and I will propose analyze a segment of a soccer game and identify its players [at least one of them]. When talking about soccer, the first thing that came to mind is look for the talented Argentinian star Lionel Messi. I will propose something: Let’s train a personalized model that allows us to locate Argentine star. Let’s analyze a real transmission [in this case I selected Uruguay-Argentina by Conmebol qualifying August 31, 2017] to see what results we can get. Let us think if it is possible to improve or contribute something to the tactics of this passionate sport, using this technology. Note: We will use TensorFlow and the proposed model for Object Detection , all under the Apache License 2.0 license. In this opportunity I will
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#BuenosAires birthplace of great developers? Analysis according #stackexchange

In this article I would like share a very simple analysis about developers and their relevance in 6 of the most important capitals of #LatAm. Based on data proposed by [http://data.stackexchange.com/] we will study the behavior of developers [all disciplines] in [Buenos Aires, Sao Paulo, Santiago de Chile, Lima, Bogota and Mexico City] I used the #Users table, the complexity begins when the analysis of the #Location attribute shows a quite variable string. Anyway it's possible identify the data of cities without problems. For this sample/snapshot I identified over 5K developers. Bogotá, Colombia 650 Lima, Perú 515 Santiago, Chile 750 Sao Paulo, Brazil 1845 Mexico City, Mexico 582 Buenos Aires, Argentina 1395 Total 5737 I used a very simple model query like this: Select count(*), Sum(reputation),Location from Users Where Location LIKE 'key%' Group by Location Order by Sum(reputation) Then using some filters... if you want avoid this step you can us
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