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.
As it turns out that I have been fortunate to participate, once again, in Interface: the podcast that my friend Rodrigo Diaz Concha manages and coordinates (link). This time, I’ve talked about one of the coolest preview products we have in Azure: Azure Notebooks.
sounds weird for a .Net Developers, however, the power, productivity, and
collaboration capabilities that Jupyter notebooks provide are something the
Python community has long taken advantage of.
I’d better leave the podcast link and hope you enjoy it (remember, is in Spanish):
Pues resulta que he tenido la suerte de participar, una vez más, en Interfaz: el podcast que dirige y coordina mi amigo Rodrigo Diaz Concha (link). En esta oportunidad, he hablado de uno de los productos en Preview que tenemos en Azure: Azure Notebooks.
Este producto suena
raro para un .Net Developers, sin embargo, la potencia, productividad y
capacidades de colaboración que proveen las Jupyter notebooks, son algo que la
comunidad de Python aprovecha desde hace tiempo.
Mejor dejo el
link del podcast y espero que lo disfruten:
Interfaz Podcast Episodio 113 – Azure Notebooks con Bruno Capuano
Free: open source software which helps ensure you are the sole person in control of your privacy
I setup this in an extra Raspberry Pi 3 that I have at home, and keep it running for the last couple of days. I was in shock when I realized that aprox 30% of my internet traffic is … not so good.
One of the cool features of PiHole, os that you can work with their logs. So I decided to apply some very powerful Machine Learning algorithms to detects anomalies and strange behaviors.
In the meantime, I decided to read the logs, and make some filters just using Excel. And I found a lot of very strange urls. Today I’ll share some of the Microsoft ones.
So, in example, do you know what does this set of urls have in common?
They are all Microsoft endpoints ! It seems that Windows 10 is sending a lot of diagnostic and other type of data. Lucky for us, most of this endpoints are well explained for each one of the Windows 10 versions. So, in example, I don’t use a lot of UWP apps, and it seems to me that the localization service does not need to send a lot of information, from a FIXED PC.
I decided to add some of this domains to the blacklist of domains and so far, so good. Windows is still working amazing, I enabled some of the urls so I can use also Visual Studio and Azure DevOps, and my user experience is still the same (with 30% less of traffic!)
So, I may want to also write about some domains I found other chatty devices uses like my Amazon Alexa, my Roku, and more … maybe in the next post! And kudos to the PiHole team!
In my last post I share some lines of code which allowed me to run some of the face recognition demos 6 times faster. I added a Frames per Second (FPS) feature in my samples. Later, thinking about performance, I realize that I don’t need to work with a full HD picture (1920 x 1080), so I added some code to resize the photo before the face detection process.
However, while I was coding arond this solution I also realized that I may want to initialize my camera to start in a lower resolution. So, I searched online on how to do this with OpenCV and I found 3 beautiful lines of code.
So, I manage to improve my processing code from 20FPS to +30FPS … which is very good ! Later on this posts I’ll try to do some similar FPS tests on a smaller device and I’ll see and share how this works.
After an amazing weekend, where I split my time supporting 2 different hackathons, today big news is the announcement of the new Raspberry Pi 4.
Did you check the news?
64-bit Quad-Core ARM Cortex-A72 processor, with a core clock speed of 1.5GHz
1GB, 2GB or 4GB of RAM
2 4K HDMI displays
starting price at $35
and much more !
I won’t get deep in details, I prefer to share some really good reviews, later i this post. Now is time to wait until the 4GB model is available in Canada and I’ll do some update on my Custom Vision docker and Machine Learning performance posts with the new device!