A COMPARATIVE ANALYSIS ON DEEP LEARNING ALGORITHMS FOR LARGE OUTPUT SPACES

Abstract

Natural language processing has a wide range of applications like voice recognition, machine translation, product 
review, aspect-oriented product analysis, sentiment classification and processing like email analysis several different 
research projects have utilized deep learning approaches in NLP. Supervised or unsupervised hierarchical deep 
learning is done with unsupervised or supervised methods in deep learning. Long Short-Term Recurrent Network 
(LSTM) and General CNN are among the most popular deep learning methods (LSTM). Beneficial in the field of AI 
and can be felt in many fields. Approaches like artificial intelligence have been particularly effective in simplifying 
the complexity and challenges in image recognition. Also aims to investigate a wide variety of deep learning topics, 
with a special focus on various methods and architectures.