Applied Explainability for Natural Language Processing Models

Question & Answering Problem with the Albert Model Introduction This blog will illustrate different explainability methods, focusing on natural language processing (NLP). You will learn how Applied Explainability techniques increase NLP models’ transparency, making them more credible and faster to develop. Task Overview Model I use the ALBERT, introduced in ALBERT: A Lite BERT for […]

Population Analysis with Applied Explainability

If you have ever worked on deploying a machine learning model, you know how challenging it is to ensure a smooth transition from your controlled environment to the real world. You need to consider many factors to achieve a successful implementation. One aspect that can be easily overlooked is the misinterpretation of samples in the […]

Year in Review: Three Hot Deep Learning Topics You May Have Missed in 2022

Deep learning is at the center of a new industrial revolution that uses artificial neural networks to create high-performing thinking machines. We are now at the point where machines are becoming better at some tasks than humans, which will revolutionize several major areas of society. This article highlights three hot deep learning areas: adversarial attacks, […]

Bridging Domain Gaps – An ADAS Dataset Application

Domain Adaptation Transfer learning is a widely adopted technique in deep learning. It involves using the weights of a pre-trained model to drastically reduce the time and costs of training a new model. One subcategory of transfer learning is domain adaptation, which refers to improving a model on a dataset that differs from the dataset […]