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 Machine Learning Engineering in Action by Ben Wilson
Before we get into how successful planning phases for ML projects are undertaken, let’s go through a simulation of the genesis of a typical project at a company that doesn’t have an established or proven process for initiating ML work.
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.
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.
From Transfer Learning for Natural Language Processing by Paul Azunre
This article discusses getting started with baselines and generalized linear models.
From AI as a Service by Peter Elger and Eóin Shanaghy
This article discusses gathering data for real-world AI projects and platforms.
| From AI as a Service by Peter Elger and Eóin Shanaghy
This article discusses how to add AI to existing platforms. |