Yannic Kilcher investigates BERT and the white paper associated with it https://arxiv.org/abs/1810.04805

Abstract:We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT representations can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE benchmark to 80.4% (7.6% absolute improvement), MultiNLI accuracy to 86.7 (5.6% absolute improvement) and the SQuAD v1.1 question answering Test F1 to 93.2 (1.5% absolute improvement), outperforming human performance by 2.0%.

Here’s an in-depth look at doing Natural Language Processing in the three top frameworks: TensorFlow, PyTorch, and Keras.

Before beginning a feature comparison between TensorFlow vs PyTorch vs Keras, let’s cover some soft, non-competitive differences between them. Non-competitive facts Below we present some differences between the three that should serve as an introduction to TensorFlow vs PyTorch vs Keras. These differences aren’t written in the spirit of […]

Here’s an interesting article on a deep learning toolkit for NLP.

Why are the results of the latest models so difficult to reproduce? Why is the code that worked fine last year not compatible with the latest release of my deep learning framework? Why is a baseline benchmark meant to be straightforward so difficult to set up? In today’s world, […]

Here’s an interesting story about data analytics, specifically NLP, and data visualization can breathe new life into classic works of literature.

Phil Harvey, a Cloud Solution Architect at Microsoft in the UK, used the company’s Text Analytics API on 19 of The Bard’s plays. The API, which is available to anyone as part of Microsoft’s Azure Cognitive Services, can be used to identify sentiment and topics in text, as well as pick out key phrases and entities. This API is one of several Natural Language Processing (NLP) tools available on Azure.

As an added bonus, I think there should be an AMC series set in Elizabethan times mirroring the events of Breaking Bad.