larq : open-source Python library for training neural networks with extremely low-precision weights and activations, such as Binarized Neural Networks. Basically, this framework is aiming at embedded / FPGA / ASIC machine learning models deployment. A fantastic resource and great model zoo on top of that.
Cloudy with a chance of DBMS : Databases are going to embedded more and more machine learning solution. Big query from Google already does that. But it's just a question of time for most mainstream DB to offer ML service.
The Next Decade in AI : Paper by Gary Marcus where he explores the possible future of AI over the next decade
1 on 1 meeting questions : a collection of 1:1 questions, great list that can help any manager pick the right question for the right context. As long as you are able to read the room/ team/ person.
Trax: advanced google deep learning library built on top of JAX. It is actively used by the DeepMind team and aiming code clear while providing advanced models like Reformer.
The Effects of Prize Structures on Innovative Performance : how to incentivize innovation? Well, the authors found that a winner-takes-all compensation scheme generates significantly more novel innovation relative to a compensation scheme that offers the same total compensation, but shared across the ten best innovations. However, like every psychological paper, you have to take it with a grain of salt.. reproducibility is always difficult.
nfstream : Python package providing fast, flexible, and expressive data structures designed to make working with online or offline network data [github]
Neural Networks on embedded systems : a good overview of the challenges and available neural network architectures for running on embedded systems.
ripgrep : grep search in PDFs, E-Books, Office documents, zip, tar.gz, etc.
pymedserver : a server framework for mobile data collection and machine learning in healthcare
fastai : fantastic machine learning library trying to abstract away a lot of PyTorch into simple API and building blocks. Sometimes it attracts a bit too much, especially with you want to get murky with some details. But all in all, fastAI is really a framework you want to look at if you are doing machine learning.
Meaningful Availability : google folks propose a different interpretation of availability in this paper. The authors propose a new metric called "windowed user-uptime". The objective of this metric is to measure user perceived uptime combined with calculating the availability over many windows in order to identify transient vs long periods unavailability.
riskquant : Netflix Python library for quantifying risk
olric : Distributed cache and in-memory key/value data store. This can be embedded as a go library.
Pilsung Kang : a lot of really cool git repository for machine learning and datascience lecture , notes, code etc.. by Pilsing Kang of the School of Industrial Management Engineering Korea University.
Developer Roadmaps : Step by step guides and paths to learn different tools or technologies, checkout the devops one .. you probably need two lifetime to cover everything.
