BYOD: a Reddit scrape experience Photo by Firmbee.com on Unsplash

In this blog post, we will explore recurrent neural networks (RNNs) and word embedding models to build your first RNN model. Photo by Markus Spiske on Unsplash

High-Quality translations between 200 languages Previously in July, Facebook AI Research (FAIR) released their most recent model in the language generation field, especially language translation, called No Language Left    

In this article, which was written in collaboration with Helyne Adamson and Sridhar G Kumar, we will discuss neural network optimizers and a practical way to speed-up the learning process, using Cyclical Learning Rate. Traditionally, the learning rate of a neural network that is    

This article shows Natural Language Processing example using Intel OpenVINO on Android Phone with ARM CPU. An application is able to record audio samples from microphone and predict one of the commands: {Yes, No, Up, Down, Left, Right, On, Off, Stop, Go}.

Real-world applications frequently seek to solve a general form of the Entity Matching (EM) problem to find associated entities. Such scenarios, which we call Generalized Entity Matching (GEM), include matching jobs to candidates in job targeting, matching students with courses in online education, matching products with user reviews on e-commercial websites, and beyond. These tasks impose new requirements such as matching data entries with diverse formats or having a flexible and semantics-rich matching definition. Scenarios like these are    

The majority of the difficulties come from data complexity as well as features like sparsity, variety, and dimensionality, and therefore the dynamic properties of the datasets. NLP continues to be a young technology; therefore, there’s plenty of room for engineers and businesses to tackle the many unsolved problems that include deploying NLP systems.