Deep Learning for NLP: Introduction

In recent times natural language processing (NLP) has seen many advancements most of which are in deep learning models. NLP as a problem is very complicated and deep learning models can handle that scale and complication with many different variations of neural network architecture. Deep learning also has a broad spectrum of frameworks that supports NLP problem solving out-of-the-box. Explore the basics of deep learning and different architectures for NLP-specific problems. Examine other use cases for deep learning NLP across industries. Learn about various tools and frameworks used such as – Spacy TensorFlow PyTorch OpenNMT etc. Investigate sentiment analysis and explore how to solve a problem using various deep learning steps and frameworks. Upon completing this course you will be able to use the essential fundamentals of deep learning for NLP and outline its various industry use cases frameworks and fundamental sentiment analysis problems.