Sentiment analysis is especially valuable when acting on social media data sources.
Deep learning is another technology that’s growing in popularity as a powerful machine learning technique that learns multiple layers of representations or features of the data and yields prediction results.
The paper also focuses on the precedents of these classes of models, examining how the initial ideas are assembled to construct the early models and how these preliminary models are developed into their current forms.
Recent Advances in Recurrent Neural Networks Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data.
RNNs consist of a stack of non-linear units where at least one connection between units forms a directed cycle.
A well-trained RNN can model any dynamical system; however, training RNNs is mostly plagued by issues in learning long-term dependencies.Deep Learning: An Introduction for Applied Mathematicians As a mathematician myself, I like to see tutorials that represent data science topics in light of their connections to applied mathematics.This paper provides a good introduction to the basic ideas that underlie deep learning from an applied mathematics perspective.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.A New Backpropagation Algorithm without Gradient Descent We’ve all been taught that the backpropagation algorithm, originally introduced in the 1970s, is the pillar of learning in neural networks.In turn, backpropagation makes use of the well-known first-order iterative optimization algorithm known as , which is used for finding the minimum of a function.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).On the Origin of Deep Learning This paper is a comprehensive historical review of deep learning models.It covers the genesis of artificial neural networks all the way up to the models that dominate the last decade of research in deep learning like convolutional neural networks, deep belief networks, and recurrent neural networks.