But it takes 11 minutes to read the tutorial…

How to Grow Neat Software Architecture out of Jupyter Notebooks – jupyter notebooks is a very common tool used by data scientist. However, the gap between this code to production or to reusing it is sometimes big. How can we over come this gap? See some ideas in this post.

https://github.com/guillaume-chevalier/How-to-Grow-Neat-Software-Architecture-out-of-Jupyter-Notebooks

High-performance medicine: the convergence of human and artificial intelligence – a very extensive survey of machine learning use cases in healthcare.

https://www.nature.com/articles/s41591-018-0300-7

New Method for Compressing Neural Networks Better Preserves Accuracy – a paper by Amazon Alexa team (mainly). Deep learning models can be huge and the incentive of compressing them is clear, this paper show how to compress the networks while not reducing the accuracy too much (1% vs 3.5% of previous works). This is mainly achieved by compressing the embedding matrix using SVD.

https://developer.amazon.com/blogs/alexa/post/a7bb4a16-c86b-4019-b3f9-b0d663b87d30/new-method-for-compressing-neural-networks-better-preserves-accuracy

Translating Between Statistics and Machine Learning – different paradigms sometimes use different terminology for the same ideas. This guide tries to bridge the terminology gap between statistics and machine learning.

https://insights.sei.cmu.edu/sei_blog/2018/11/translating-between-statistics-and-machine-learning.html

Postmake – “A directory of the best tools and resources for your projects”. I’m not sure how best is defined but samplig few categories it seems good (e.g. development categorty is pretty messy including github, elasticsearch and sublime together). I liked the website design and the trajectory. I do miss some category of task managment ( couldn’t find Jira and any.do is not really a calender). It is at least good resource for inspiration.

https://postmake.io