Category Archives: PyTorch

I’m currently on a multi-week mission to explore the Hugging Face (HF) code library for Transformer Architecture (TA) systems for natural language processing (NLP) and today I did a question-answer (QA) example. Whew! That’s a lot of acronyms in an … Continue reading →

Deep neural systems based on Transformer Architecture (TA) have revolutionized the field of natural language processing (NLP). Unfortunately, TA systems are insanely complex, meaning that implementing a TA system from scratch is not feasible, and implementing TA using a low-level … Continue reading →

In a standard classification problem, the goal is to predict a class label. For example, in the Iris Dataset problem, the goal is to predict a species of flower: 0 = “setosa”, 1 = “versicolor”, 2 = “virginica”. Here the … Continue reading →

When I create neural software systems, I most often use the PyTorch library. The Keras library is very good for basic neural systems but for advanced architectures I like the flexibility of PyTorch. Using raw TensorFlow without Keras is an … Continue reading →

A regular neural network has a set of numeric constants called weights which determine the network output. If you feed the same input to a regular trained neural network, you will get the same output every time. In a Bayesian … Continue reading →

Ordinal classification, also called ordinal regression, is a multi-class classification problem where the class labels to predict are ordered, for example, 0 = “poor”, 1 = “average”, 2 = “good”. You could just do normal classification, but then you don’t … Continue reading →