Category Archives: Keras

One of the major challenges of machine learning with TensorFlow, Keras, and PyTorch is that these libraries are under continuous development and so breaking changes are introduced every few months. I try to revisit core example programs as often as … Continue reading →

The most common way to define a simple Keras neural network network/model is to just crank it out as a sequence of Dense layers. For example, to create a 4-5-3 network for the Iris Dataset where the labels are already … Continue reading →

One Sunday morning, after I walked my dogs, I decided to code up an example of using a Keras library autoencoder for dimensionality reduction for visualization of the UCI Digits dataset. The UCI Digits dataset consists of crude 8×8 images … Continue reading →

Deep neural systems based on Transformer Architecture (TA, also called multi-headed attention models) have revolutionized natural language processing (NLP). TA systems were designed to deal with sequence-to-sequence problems, such as translating English text to German text. TA systems can also … Continue reading →

An ordinal classification problem (confusingly, also called ordinal regression) is one where the goal is to predict a class label in situations where the labels have an ordering. For example, you might want to predict the price of a house, … Continue reading →

It’s a major challenge to keep up with the continuous changes to the Keras/TensorFlow neural code library (and the PyTorch library too). I recently upgraded my Keras installation to version 2.6 and so I’m going through all my standard examples … Continue reading →