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.

GPT-3, also standing for Generative Pre-trained Transformer 3, is one of the latest AI models by OpenAI. Many say that it will revolutionize the world of Artificial Intelligence, and while this is a slight over-exenteration, the truth is that GPT-3 indeed does poses some impressive abilities.

Big language models are a new trend that is about to launch and influence NLP and search engines. NLP with advances in Deep Learning is getting behind fascinating use cases, like question answering in healthcare and finance. Haystack platform enables you to create flexible search pipelines, starting with document conversions (say, pdf to text), extracting metadata useful for search & filter later, proceeding to semantic indexing and similarity matching. You can combine sparse retrieval with dense search, all