Sometimes TensorFlow will seem to be installed correctly with pip only to fail when you try to import the package with the message…
A troubleshooting guide for one of the more frustrating TensorFlow exceptions: the dreaded ModuleNotFoundError.
The Keras dense layer can be a little confusing. This post will give you everything you need to start using it.
Sometimes you need to define your own Keras custom layer. This tutorial explains how custom layers work for tensorflow>=1.7.0 (up to at least 2.4.0) which includes a fairly stable version of the Keras API.
There are lots of ways to install TensorFlow, which means (unfortunately) there is no one-size-fits-all solution for uninstalling it.
Building machine learning pipelines as well-formed Python packages simplifies transfer learning. Here’s a simple example.
How you structure code in an ML pipeline makes a big difference in whether other people can easily use it. Here’s a recommendation for how to do it well.