AI, Machine Learning and Data Science Roundup: January 2019

January 16, 2019

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

A monthly roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software announcements and data applications from Microsoft and elsewhere that I've noted over the past month or so.

Open Source AI, ML & Data Science News

Preview of Tensorflow 2.0 (the public preview is expected "early this year").

pcLasso, an R package implementing a new method for supervised learning described by co-author Rob Tibshirani as "principal components regression meets the lasso".

R 3.5.2 has been released.

Industry News

A retrospective of Google Research activities in AI in 2018.

Google Cloud Platform now supports R jobs (via SparkR) in Cloud DataProc.

GCP App Engine support for Python 3.7 now generally available.

Amazon SageMaker now comes preconfigured with support for SciKit-learn.

Microsoft News

Microsoft Professional Program for Data Analysis, a new online course and certification. Other MPP tracks include Data Science and Artificial Intelligence.

A single API key can now be used to access Cognitive Services APIs for language, vision and search.

AzureR, a suite of packages for interfacing with storage, virtual machines, containers and other Azure services from the R language, is now available on CRAN.

Learning resources

E-book by TWIML AI host Sam Charrington: Kubernetes for Machine Learning, Deep Learning and AI (requires free sign-up).

E-book by Patrick Hall and Navdeep Gill: Introduction to Machine Learning Interpretability (requires free registration).

An in-depth introduction to convolutional neural networks, from Ars Technica: How computers got shockingly good at recognizing images.

An on-line course from Databricks, Deep Learning Fundamental Series, with a focus on Keras and TensorFlow.

Getting Started with TensorFlow Probability, from R, a blog post from RStudio.

What can Neural Networks Learn?, an approachable look at the inner workings of neural network classifiers from Brandon Rohrer.

A blog post describing the computational graph concepts behind TensorFlow, including an illustrative implementation of core TensorFlow operations in numpy.


Using computer vision to monitor shelf stocking policies, a three-part series on complex image classification: Part 1, Part 2, Part 3.

Building a pet breed identification application via transfer learning with Azure ML Service and Python. (Github repo here.)

Find previous editions of the monthly AI roundup here.

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