Linus Torvalds transformed technology twice — first with the Linux kernel and again with Git.

In a rare interview with TED Curator Chris Anderson, Torvalds discusses with remarkable openness the personality traits that prompted his unique philosophy of work, engineering and life.

He also has the same kind of treadmill desk that I have. Winking smile

“I am not a visionary, I’m an engineer,” Torvalds says. “I’m perfectly happy with all the people who are walking around and just staring at the clouds … but I’m looking at the ground, and I want to fix the pothole that’s right in front of me before I fall in.”

The Data Science & AI community has truly embraced open source. In fact, there are so many libraries and tools out there, that it can be challenging to keep up. Fortunately, here’s a great round up of 21 open source tools for Machine Learning. Some of them you may have heard of, but I guarantee there are a few surprises in this list, even for the seasoned expert.

  • Presenting 21 open source tools for Machine Learning you might not have come across
  • Each open-source tool here adds a different aspect to a data scientist’s repertoire
  • Our focus is primarily on tools for five machine learning aspects – for non-programmers(Ludwig, Orange, KNIME), model deployment(CoreML, Tensorflow.js), Big Data(Hadoop, Spark), Computer Vision(SimpleCV), NLP(StanfordNLP), Audio, and Reinforcement Learning(OpenAI Gym)

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.

Here’s an interesting perspective on what blockchain and open source have in common and how they will enrich each other in the years to come.

The many similarities between blockchain and the open source are not just a coincidence. Analysts and developers believe that the new technology is picking up from where open source left off. There is a limit to what companies can share with open source. Open source is not known to open up live systems and it can never open their data.

Over the last decade or so, open source has blossomed into a major movement and the backbone of the tech industry. For instance, check out this project that Uber, yes Uber, has open sourced.

Ludwig is a TensorFlow-based toolbox that allows you to train and test deep learning models without the need to write any of the code. Incubated at Uber for the last two years, Ludwig was finally open sourced this February to incorporate the contributions of the data science community. With Ludwig, a data scientist can train a deep learning model by simply providing a CSV file that contains the training data as well as the YAML file with the outputs and inputs of the model.

Over the last few years, cloud computing has become core to many enterprise IT models, and a number of enterprise architects are trying to make cloud systems as effective and beneficial as possible.

What role does Open Source play in making enterprises more agile to market fluctuations and the ability to seize opportunities? Given the footprint of open source tooling in big data and AI, this is not strictly a concern for infrastructure folks and app developers.

Enterprise Cloud Platforms for business This article covers the characteristics of the open source cloud and the open source based cloud computing layers, to help enterprises make the right choice. An open source cloud is any cloud service or solution that is developed using open source technologies and software. […]

Last last week, Google’s AI research division open-sourced GPipe, a library for “efficiently” training deep neural networks under Lingvo, a TensorFlow framework for sequence modeling.

Most of GPipe’s performance gains come from better memory allocation for AI models. On second-generation Google Cloud tensor processing units (TPUs), each of which contains eight processor cores and 64 GB memory (8 GB per core), GPipe reduced intermediate memory usage from 6.26 GB to 3.46GB, enabling 318 million parameters on a single accelerator core. Without GPipe, Huang says, a single core can only train up to 82 million model parameters.

Armon Dadgar (@armon), HashiCorp CTO and co-founder, and Aaron Schlesinger (@arschles) talk about how and why HashiCorp Vault is a security and open source product: two things traditionally considered at odds. Learn how to avoid secret sprawl and protect your apps’ data, ways for contributors and maintainers to enhance the security of any project, and why you should trust no one (including yourself).

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