Science Research Papers

Science Research Papers-41
Along with the success of deep learning in many other application domains, deep learning is also finding common use in sentiment analysis in recent years.This paper provides an informative overview of deep learning and then offers a comprehensive survey of its current application in the area of sentiment analysis.RNNs consist of a stack of non-linear units where at least one connection between units forms a directed cycle.

Along with the success of deep learning in many other application domains, deep learning is also finding common use in sentiment analysis in recent years.This paper provides an informative overview of deep learning and then offers a comprehensive survey of its current application in the area of sentiment analysis.RNNs consist of a stack of non-linear units where at least one connection between units forms a directed cycle.

Against a background of considerable progress in areas such as speech recognition, image recognition, and game playing, AI contrarian Gary Marcus of New York University presents ten concerns for deep learning, and suggests that deep learning must be supplemented by other techniques if we are to reach the long-term goal of .

The Matrix Calculus You Need For Deep Learning This paper is a wonderful resource that explains all the linear algebra you need in order to understand the operation of deep neural networks (and to read most of the other papers on this list).

This paper presents a survey on RNNs and highlights several recent advances in the field.

Deep Learning: A Critical Appraisal Although deep learning has historical roots going back decades, neither the term “deep learning” nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton’s now classic 2012 paper “Image Net Classification with Deep Convolutional Neural Networks.” What has the field discovered in the five subsequent years?

In this paper, Bangalore-based PES University researchers describe an alternative to backpropagation without the use of Gradient Descent.

Instead, they devise a new algorithm to find the error in the weights and biases of an artificial neuron using .

GN divides the channels into groups and computes within each group the mean and variance for normalization.

GN’s computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes.

The paper features numerical studies and experiments performed on various data sets designed to verify that the alternative algorithm functions as intended.

Deep Learning for Sentiment Analysis : A Survey Sentiment analysis is a widely used process of computationally identifying and categorizing opinions expressed in a piece of text, in order to determine whether the writer’s attitude towards a particular topic, product, etc., is positive, negative, or neutral.

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