classification
![Brian Piercy](data:image/svg+xml;utf8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="%230A66C2"><path d="M20.447 20.452H16.89v-5.569c0-1.328-.027-3.037-1.852-3.037-1.853 0-2.136 1.445-2.136 2.939v5.667H9.351V9h3.414v1.561h.046c.477-.9 1.637-1.85 3.37-1.85 3.601 0 4.267 2.37 4.267 5.455v6.286zM5.337 7.433a1.982 1.982 0 1 1 0-3.964 1.982 1.982 0 0 1 0 3.964zm1.782 13.019H3.555V9h3.564v11.452zM22.225 0H1.771C.792 0 0 .774 0 1.729v20.542C0 23.227.792 24 1.771 24h20.451C23.2 24 24 23.227 24 22.271V1.729C24 .774 23.2 0 22.222 0h.003z"/></svg>)
Learn about implementing Softmax from scratch and discover how to avoid the numerical stability trap in deep learning projects.
The key challenge lies in finding the right balance: How cautious should our model be when making classifications?
Why do we use the logistic and softmax functions? Thermal physics may have an answer.
An algorithmic approach to clean up your dataset and sharpen class assignments.
BERT Transformer & Food-Drug Negative Interactions
Manual Calculation From a Confusion Matrix and the Syntax of sklearn Library
Classifying cross-topic natural language texts based on their argumentative structure using deep learning
Traditionally, most of the multi-class classification problems (i.e. problems where you want to predict where a given sample falls into, from a set of possible results) focus on a small number of possible predictions.
Lately I’ve been thinking a lot about the connection between prediction models and the decisions that they influence. There is a lot of theory around this, but communicating how the various pieces all fit together with the folks who will use and be impacted by these decisions can be challenging. One of the important conceptual pieces […]