Intro: Knime is a very powerful machine learning tool, particularly suitable for the management of complicated workflows as well as rapid prototyping. It has recently become yet more useful with the arrival of easy-to-use Python nodes. This is true because sometimes the set of nodes – which is large – still may not provide the […]
In this follow up post we apply the same methods we developed previously to a different dataset. In this third post we mix the previous two datasets. So, on the one hand, we have noise signals, on the other hand we have innovators and followers.
In this follow up post we apply the same methods we developed previously to a different dataset.
In this post we present the results of a competition between various forecasting techniques applied to multivariate time series. The forecasting techniques we use are some neural networks, and also – as a benchmark – arima. In particular the neural networks we considered are long short term memory (lstm) networks, and dense networks. The winner in the […]
Intro In this post we report success in using reinforcement to learn the game of nim. We had previously cited two theses (ERIK JÄRLEBERG (2011) and PAUL GRAHAM & WILLIAM LORD (2015)) that used Q-learning to learn the game of nim. However, in this setting, the scaling issues with Q-learning are much more severe than with value-learning. In […]
There are entire theses devoted to reinforcement learning of the game of nim, in particular those of ERIK JÄRLEBERG (2011) and PAUL GRAHAM & WILLIAM LORD (2015). Those two were successful in training a reinforcement-based agent to play the game of nim with a high percentage of accurate moves. However, they used lookup tables as their evaluation functions, which […]
This post reports on the creation of a python package for the game of nim. The package contains a function that finds the perfect move in a given position and informs on whether the position is winning. In the package we make use of Charles Leonard Bouton’s solution of the game of Nim. We use […]
So now we’re ready to deploy our own custom R Shiny server with caching. We had previously already discussed the pros and cons of hosting your own server, by this we mean a docker based server in the cloud Signing up at https://www.shinyapps.io/ See this https://arthought.com/r-shiny-stock-analysis. Then we had opted for option 2, mainly to […]
In the previous post we looked at a simple data caching example which we used to explore the workings of the R-package DataCache. In this post we continue with this exploration. Instead of just using system time as the datafeed we now use a more real world example of financial data. This is again in […]
This is in preparation for running a custom Shiny server. We want to accelerate the server by using caching. In the this post we take a look at a candidate caching package. In this post we’ll explore a the package DataCache. It is a very useful package, however, I found that for some reason the provided […]