Matrix operations with pytorch – optimizer – addendum

This blog post is an addendum to a 3 post miniseries​1​.  Here were present 2 notebooks. 02a—SVD-with-pytorch-optimizer-SGD.ipynb Is a dropin replacement of the stochastic gradient + momentum method shown earlier​2​, but with using the inbuilt pytorch sgd optimiser. 02b—SVD-with-pytorch-optimizer-adam.ipynb Uses the inbuild pytorch adam optimizer – rather than the sgd …

Matrix operations with pytorch – optimizer – part 3

SVD with pytorch optimizer This blog post is part of a 3 post miniseries.  Today’s post in particular covers the topic SVD with pytorch optimizer. The point of the entire miniseries is to reproduce matrix operations such as matrix inverse and svd using pytorch’s automatic differentiation capability. These algorithms are …

Matrix operations with pytorch – optimizer – part 2

pytorch – matrix inverse with pytorch optimizer This blog post is part of a 3 post miniseries.  Today’s post in particular covers the topic pytorch – matrix inverse with pytorch optimizer. The point of the entire miniseries is to reproduce matrix operations such as matrix inverse and svd using pytorch’s …

Matrix operations with pytorch – optimizer – part 1

pytorch – playing with tensors This blog post is part of a 3 post miniseries.  The point of the entire miniseries is to reproduce matrix operations such as matrix inverse and svd using pytorch’s automatic differentiation capability. These algorithms are already implemented in pytorch itself and other libraries such as …

Knime – Multivariate time series

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 – …

Multivariate Time Series Forecasting with Neural Networks (3) – multivariate signal noise mixtures

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.

Multivariate Time Series Forecasting with Neural Networks (1)

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 …