Deep Learning has enabled all sorts of innovations, but what would happen if more people (ie. non developers & ML engineers) had access to this technology. That’s the goal of Ludwig.

Ludwig is a toolbox built on top of TensorFlow that allows anyone to train and test deep learning models without any code. It provides a datatype based approach to develop a predictive deep learning models suitable for a wide range of applications.

In essence, it will allow non-experts to develop and integrate deep learning models in their website or app to get the full benefit of a deep learning system that will help them accelerate their business or improve their lives. These non-experts and even experts for that matter can leverage this powerful new technology to dramatically reduce the time spent training and testing deep learning models and instead “Focus on developing deep learning architectures rather than data wrangling” to quote the Uber researchers from their blog post.

Here’s part one of a series of blog posts that will explore the machine learning options available in Azure.

In this post series, I am going to show how we can use Azure Machine learning services and the new features added that make life so easy to train, deploy, automate managing machine learning models [1]. In this post, first I will show how to use a no code environment for Auto ML, how to access it and some difference between Azure mL Studio and services.

Readers of Frank’s World know at least two things about me: I love data and I love learning. In 30 months, earned 41 certifications in Data Science, Data Engineering, and AI. All of this wouldn’t have been possible without the innovation of online learning.

Here’s a great deal: the Complete Machine Learning A to Z Bundle has 9 courses for just $35. These courses are designed to help you become proficient with these important technologies.

Deep learning isn’t just about helping computers learn from data—it’s about helping those machines determine what’s important in those data sets. This is what allows for Tesla’s Model S to drive on its own or for Siri to determine where the best brunch spots are. You’ll learn TensorFlow, Python’s scikit-learn, Apache MXNet, PyTorch, and more. You’ll learn how to build chatbots with Google DialogFlow and Amazon Lex, and you’ll learn to build voice apps for Amazon Lex. It’s on sale for $35.

Speaking of open source, here’s a list of the top 10 open source projects on GitHub.

Every year, the GitHub community digs deeper into open source projects and extracts the top open source projects by the contributor count. The information on this article has been cited from the original documentation and the sources are also cited inside. Here are the top 10 open source projects […]

Consider the following opening paragraph in this Yahoo! Finance story.

Open-source software once represented a threat for Microsoft, but as the company has recalibrated to focus more on cloud services, its has come to embrace open-source.

And did you ever think that this would happen?

Microsoft is now a major contributor of open-source code on GitHub.

As someone who has been in the Microsoft community since the early 2000s and an employee from 2011 to 2016 (and again for the past year), this change is nothing short of remarkable.

The first Build conference happened in 2011 and, let me tell you, it was a very different kind of event than the one going on this week. In fact, in 2016 at the Build conference in 2016, I wondered what would someone who attended in 2011 who had been in a coma for five years have thought if they saw “Microsoft <3 Linux” stickers at an official booth. They would have likely thought that they had entered a parallel universe.

This mind shift is nothing less of extraordinary and some would even say miraculous. And the world at large is starting to take notice.