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Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives [Discussion Forum]

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In this article, the authors bring together diverse perspectives, coming from different geographical locations with different core research expertise and different affiliations and work experiences, to share the data analytics opinions and perspectives of the authors relating to the new opportunities and challenges brought forth by the big data movement.
Abstract: 
"Big Data" as a term has been among the biggest trends of the last three years, leading to an upsurge of research, as well as industry and government applications. Data is deemed a powerful raw material that can impact multidisciplinary research endeavors as well as government and business performance. The goal of this discussion paper is to share the data analytics opinions and perspectives of the authors relating to the new opportunities and challenges brought forth by the big data movement. The authors bring together diverse perspectives, coming from different geographical locations with different core research expertise and different affiliations and work experiences. The aim of this paper is to evoke discussion rather than to provide a comprehensive survey of big data research.

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Big data and predictive analytics for supply chain and organizational performance

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Learning in Nonstationary Environments: A Survey

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The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.

TL;DR: This report presents a brief survey on development of DL approaches, including Deep Neural Network (DNN), Convolutional neural network (CNN), Recurrent Neural network (RNN) including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).
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References
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The Structure and Function of Complex Networks

Mark Newman
- 01 Jan 2003 - 
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Journal ArticleDOI

Reducing the Dimensionality of Data with Neural Networks

TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
MonographDOI

Causality: models, reasoning, and inference

TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
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Learning Deep Architectures for AI

TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.
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