Preparing multilabel dataset for training ConvNet with Caffe

Preparing multilabel training set for caffe framework is a bit nontrivial. So, if you have multiple, possibly varying number of ground truth labels for each training example then here is how you can do it using LMDB store. For LMDB data source you need to separate your data input and your labels by creating two LMDB (one for the data and the second one for the labels). You also have to define two data layers in your network definition, set the same batch size for both of them and disable shuffling for the alignment.

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Sentiment analysis with CoreNLP

In the rise of social media customer’s opinions has become extremely valuable for businesses selling their products, financial markets and social researches. To extract opinions from customer’s reviews, comments or other kind of text data you might want to know what sentiment analysis is.

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