Proceedings ArticleDOI
Augean Artificial Neural Network
TLDR
Artificial neural network and neuroscience have been used to make the model more robust increasing the accuracy with at least five percent to the previous models involved.Abstract:
There is always a big influence of robotics and natural language processing in the field of automation. The current scenario of natural language processing (NLP) focuses on machine analysis and machine interpretation. By the term machine analysis, accuracy becomes an important phenomenon. So, it is named Augean that refers to higher accuracy for natural language processing which is difficult to attain involving the use of neural networks. Thus far named as Augean artificial neural network. It is one of the key branches of machine learning which is peculiar from the other branches speculating more towards artificial intelligence. NLP is now one of the key aspects in making machine learn and letting it speak. The current framework of these discusses more of deep learning. One of the biggest challenges is to make the machine learn and speak is known. Thus far processing becomes the most crucial part. Therefore, when it comes to processing there is no better idea than the brain to be involved. Now the current scenario is about making machine learn to the best of the accuracy and if there is something of great accuracy then it involves a real complex algorithm. The machine works possibly best only if the accuracy stays high and stepping on to the precision of work with better learning ability machines are remarkable. So, to frame this, artificial neural network and neuroscience have been used to make the model more robust increasing the accuracy with at least five percent to the previous models involved.read more
References
More filters
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Posted Content
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
TL;DR: This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning.
Journal ArticleDOI
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
TL;DR: This paper reviews significant deep learning related models and methods that have been employed for numerous NLP tasks and provides a walk-through of their evolution.
Journal ArticleDOI
State-of-the-art in artificial neural network applications: A survey
Oludare Isaac Abiodun,Oludare Isaac Abiodun,Aman Jantan,Abiodun Esther Omolara,Kemi Victoria Dada,Nachaat AbdElatif Mohamed,Humaira Arshad +6 more
TL;DR: The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems and proposed feedforwardand feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance.