Tag: Jupyter

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 …

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 …

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 …

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 …

This is a follow up to https://arthought.com/comparison-of-a-very-simple-regression-in-tensorflow-and-keras/ It covers the same topic but in pytorch.

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 …