From Deep Learning for Natural Language Processing by Stephan Raaijmakers
This article introduces you to working with BERT.
From Automated Machine Learning in Action by Qingquan Song, Haifeng Jin, and Xia Hu
This article covers
• Defining and introducing the fundamental concepts of machine learning
• Describing the motivation for and high-level concepts of automated machine learning
From Deep Learning for Natural Language Processing by Stephan Raaijmakers
This article covers multitask learning for NLP.
From Graph-Powered Machine Learning by Alessandro Negro
This article discusses managing data in graph-powered machine learning projects.
By Graph-Powered Machine Learning Alessandro Negro
This article discusses creating a bigraph for a user-item dataset.
From Transfer Learning for Natural Language Processing by Paul Azunre
In this article, we cover some representative deep transfer learning modeling architectures for NLP that rely on a recently popularized neural architecture – the transformer – for key functions.
From Getting Started with Natural Language Processing by Ekaterina Kochmar
This article shows you how to extract the meaningful bits of information from raw text and how to identify their roles. Once you have roles identified, you can move on to syntactic parsing.
In this video, Hobson shows you how to move words from inflammatory to less inflammatory context with the help of word vectors (Word2vec).
From Getting Started with Natural Language Processing by Ekaterina Kochmar
This article shows you how to extract the meaningful bits of information from raw text and how to identify their roles. Let’s first look into why identifying roles is important.
From Transfer Learning for Natural Language Processing by Paul Azunre
This article delves into using shallow transfer learning to improve your NLP models.