Machine learning with the maximum of free GPU currently available plus the ability to keep a neat log of your data science experiments. Interested? My article shows a deep dive solution. Quick summary Colab, MLflow and papermill are individually great. Together they form a dream team. Colab is great for …
This post is a follow-up of 1. We start off with an eye-catching plot, representing the functioning of an optimiser using the stochastic gradient method. The plot is explained in more detail further below. Visualisation of a loss surface with multiple minima. The surface is in gray, the exemplary path …
In this short post we perform a comparative analysis of a very simple regression problem in tensorflow and keras. We start off with an eye-catching plot, representing the functioning of an optimizer using the stochastic gradient method. The plot is explained in more detail further below. A 3 rotatable …
The previous post was already about dash. So why return to the subject? In some ways I got carried away by the possibilities of dash. I therefore included some concepts that are nice, by themselves, however introduce a level of complexity that is not fully necessary to start you first …
Dash is an amazing dasboarding framework. If you’re looking for an easy-to- setup dashboarding framework that is able to produce amazing plots that wow your audience, chances are that this is your perfect fit. Further it is also friendly to your CPU. The solution I will show here is running simultaneously on the same 5€/month digital ocean instance as the WordPress installation hosting the article you’re reading.
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 – …
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 …
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 …