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While watching a recent webinar sponsored by The ACM, “Break Into AI: A Q&A with Andrew Ng on Building a Career in Machine Learning,” I found out that Dr.Ng routinely carries around a folder of research papers that he can draw from when there’s a lull in his active schedule like when he’s riding in an Uber.Multilayered artificial neural networks are becoming a pervasive tool in a host of application domains.
It assumes little math knowledge beyond what you learned in freshman calculus, and provide links to help you refresh the necessary math where needed.
Note that you do not need to understand this material before you start learning to train and use deep learning in practice; rather, this material is for those who are already familiar with the basics of neural networks, and wish to deepen their understanding of the underlying math.
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.
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).
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.