BERT in Natural Language Processing
Channel: Victor Panlilio Photography
Date: 19/01/2020

Jesse Moore (https://www.linkedin.com/in/jessemoore07/) explains "Using BERT To Accelerate Natural Language Processing". Data collection is burdensome, time-consuming, expensive, and the number one limiting factor for successful NLP projects. Preparing data, building resilient pipelines, making choices amongst hundreds of potential preparation options, and getting “model ready” can easily take months of effort even with talented machine learning engineers. Finally, training and optimizing deep learning models require a combination of intuitive understanding, technical expertise, and an ability to stick with a problem. Google's BERT (Bidirectional encoder representations from transformers) is a form of transfer learning that requires far less data and training time. Building world class models become the possibility of any data scientist, rather than solely the domain of large companies with large budgets.