Neural networks have been proved efficient in improving many machine learning tasks such
as convolutional neural networks and recurrent neural networks for computer vision and …

This Paper reveals the information about Deep Neural Network (DNN) and concept of deep
learning in field of natural language processing ie machine translation. Now day's DNN is …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Hyperparameters are important for machine learning algorithms since they directly control
the behaviors of training algorithms and have a significant effect on the performance of …

Deep learning methods employ multiple processing layers to learn hierarchical
representations of data, and have produced state-of-the-art results in many domains …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

Many existing deep learning models for natural language processing tasks focus on
learning the compositionality of their inputs, which requires many expensive computations …

With rapid developments in the design of deep architecture models and learning algorithms,
methods referred to as deep learning have come to be widely used in a variety of research …

In this paper, we investigate the problem of learning a machine translation model that can
simultaneously translate sentences from one source language to multiple target languages …

We introduce a model for constructing vector representations of words by composing
characters using bidirectional LSTMs. Relative to traditional word representation models that …