One of the good things of being active in twitter is that you get exposed to interesting content. The bad thing about it is that you retweet with the goal of finding it later and read it. However, you never find it or read it afterwards.
So, here is a new attempt to catch up on interesting news, blog posts on anything related to data, data science, ML and AI that is probably not mainstream.
Here you go with the first post on “Data Science with R and Python Round Up” for this month, i.e. December 2019. The new year resolution is that to continue the monthly round up.
- Python 2 series to be retired by April 2020, In case you missed this 🙂
- R version 3.6.2 (Dark and Stormy Night)Â has been released on 2019-12-12.
- Weird Government Twitter versus Zuck, The PA Treasury takes on Facebook’s board structure, from NormCore Tech by Vicki Boykis
- Yoshua Bengio, Revered Architect of AI, Has Some Ideas About What to Build Next, The Turing Award winner wants AI systems that can reason, plan, and imagine, in IEEE Spectrum
- Introducing Tasks to Kaggle Datasets, Tasks are a way for Kagglers to pose a question or problem related to a dataset for our broader community to solve. The task can be anything you’re interested in seeing a response for, from building a predictive model to helping augment a dataset to providing a fresh analysis or exploration on the data.
- This is how you put the data in Data Science! Data Science has evolved. These 20 million datasets are proof., by Cassie Kozyrkov
- R vs. Python: What’s the best language for Data Science? from RStudio Blog (What is next for the popular programming language R? A recent interview with Hadley Wickham at Quartz.)
- On the Measure of Intelligence, by François Chollet
- Best Data Visualization Projects of 2019, by Nathan Yau
- The ‘largest stock profit or loss’ puzzle: efficient computation in R, by David Robinson
- Want to make good business decisions? Learn causality at StichFix Blog
- Sebastian Raschka‘s 3rd Edition of Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 is ready and is worth checking out.
What’s deep learning?
The “common usage” definition as of 2019 would be “chains of differentiable parametric layers trained end-to-end with backprop”.
But this definition seems overly restrictive to me. It describes *how we do DL today*, not *what it is*.
— François Chollet (@fchollet) December 26, 2019
