Welcome to our biweekly selection of Free and Open machine learning news. Created using our own opinionated selection and summary algorithm. FOSS machine learning is crucial for everyone. Machine Learning is a complex technology. So keep it simple.
1 Papers with code
If you want to know what is happing on Machine Learning research you should take a look at this great site. It is a free resource for researchers and practitioners to find and follow the latest state-of-the-art ML papers and code. All data licenced under the CC BY-SA licence. I love this. The mission of Papers With Code is to create a free and open resource with Machine Learning papers, code and evaluation tables.
2 Seeing Theory
If your really want to understanding the basics of machine learning means you must dive into probability and statistics. When I studied these subjects great teachers did their utter best to make it fun and interesting. Mathematics can be dry and boring. Great visuals and seeing things is always great when learning new complex things. But this resource is really great to study statistics. Great visuals. And the source repository is available for everyone to learn how to create such a great playbook. This resource is a great example of why learning using Internet is so much more fun than 20 years ago. Use it and share it!
3 The MLflow Project Joins Linux Foundation
Machine learning is transforming all major industries and driving billions of decisions in retail, finance, and health care. The First End-to-End Machine Learning Platform Is Embraced by the Community with over 2 Million Downloads Per Month and over 200 Contributors in Only 2 Years. MLflow, an open source machine learning (ML) platform created by Databricks, will join the Linux Foundation. “Our move to contribute MLflow to the Linux Foundation is an invitation to the machine learning community to incorporate the best practices for ML engineering into a standard platform that is open, collaborative, and end-to-end.“
4 Detecting Mismatches in Machine-Learning Systems
Integrating ML components into applications is limited by the fragility of these components and their algorithms. For example, ML capabilities can be used to suggest products to users based on purchase history; provide image recognition for video surveillance; identify spam email messages; and predict courses of action, routes, or diseases, among others. They are also limited by mismatches between different system components. For example, if an ML model is trained on data that is different from data in the operational environment, field performance of the ML component will be dramatically reduced.
(Software Engineering Institute)
5 scikit-survival 0.13 Released
scikit-survival is a Python module for survival analysis built on top of scikit-learn. It allows doing survival analysis while utilizing the power of scikit-learn, e.g., for pre-processing or doing cross-validation.
6 AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
Machine learning research has advanced in multiple aspects, including model structures and learning methods. The effort to automate such research, known as AutoML, has also made significant progress. Our goal is to show that AutoML can go further: it is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space.
7 Legal Risks of Adversarial Machine Learning Research
Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML systems such as those used in Facebook, Tesla, Microsoft, IBM, Google to demonstrate vulnerabilities. In this paper, we ask, “What are the potential legal risks to adversarial ML researchers when they attack ML systems?”
8 The machine learning community has a toxicity problem
Interesting discussion on the front page of Internet. Fact is indeed that with such a enormous number of AI/ ML papers every week, it’s impossible to read them all and quality should be questioned…
The FOSS Machine Learning News Blog is a brief overview of open machine learning news from all over the world. Free and Open machine learning means that everyone must be able to develop, test and play and deploy machine learning solutions. Read and share the FOSS ML Guide! And remember:You are invited to join the Free and Open Machine Learning open collaboration project.