Survey of deep learning and architectures for visual captioning—transitioning between media and natural languages

Abstract

Deep Learning Architectures has been researched the most in this decade because of its capability to scale up and solve problems that couldn’t be solved before. Mean while many NLP applications cropped up and there is a requirement to understand how the concepts gradually evolved till date after perceptron was introduced in 1959. This document will provide a detailed description of the computational neuroscience starting from artificial neural network and how researchers retrospected the drawbacks faced by the previous architectures and paved the way for modern deep learning. Modern deep learning is more than what it had been perceived decades ago and had been extended to architectures, with exceptional intelligence, scalability and precision, beyond imagination. This document will provide an overview of the continuation of work and will also specifically deal with applications of various domains related to natural language processing and visual and media contents.