Deep learning applications and challenges in big data analytics
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Cites background from "Deep learning applications and chal..."
...In fact, due to its ability to handle large amounts of unlabeled data, deep learning techniques have provided powerful tools to deal with big data analysis [31,122]....
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2,100 citations
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...To the authors’ knowledge, this is the first such survey in the agricultural domain, while a small number of more general surveys do exist (Deng and Yu, 2014; Wan et al., 2014; Najafabadi et al., 2015), covering related work in DL in other domains....
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...For a more elaborate description of the DL concept and its applications, the reader could refer to existing bibliography (Schmidhuber, 2015; Deng and Yu, 2014; Wan et al., 2014; Najafabadi et al., 2015; Canziani et al., 2016; Bahrampour et al., 2015)....
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References
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31,952 citations
"Deep learning applications and chal..." refers background in this paper
...Previous works used to adapt hand designed feature for images like SIFT and HOG to the video domain....
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...For example, the Histogram of Oriented Gradients (HOG) [2] and Scale Invariant Feature Transform (SIFT) [3] are popular feature engineering algorithms developed specifically for the computer vision domain....
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20,077 citations
16,989 citations
"Deep learning applications and chal..." refers background in this paper
...Previous works used to adapt hand designed feature for images like SIFT and HOG to the video domain....
[...]
...For example, the Histogram of Oriented Gradients (HOG) [2] and Scale Invariant Feature Transform (SIFT) [3] are popular feature engineering algorithms developed specifically for the computer vision domain....
[...]
16,717 citations