At the RL Summit, Sven Mika, lead maintainer for Ray RLlib, led a hands-on tutorial on RL and RLlib for recommender systems. In this blog post, we share a quick summary of the tutorial, along with a few big takeaways from the session.
This blog, Part 2, will explain how to use Ray to speed up Deep Learning forecasting when training one large global model in order to predict many target time series. We will train an LSTM version of RNN with GRN building blocks, Encoder-Decoder, and...
What is statistical forecasting and how you can use ARIMA and Prophet on Ray to speed up your forecasting.