[Links of the Day] 14/04/2020 : Time series dynamical attractors Autoencoder , Binarized Neural Network framework, Machine learning and Databases

• Deep learning of dynamical attractors from time series measurements : the authors propose a general embedding technique for time series, consisting of an autoencoder trained with a novel latent-space loss function. Worth giving it a look if you deal with time series.
• 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.