This post explains my understanding in a proposed greedy algorithm for the maximum weighted matching problem. The greedy algorithm goes as follows (listed by this paper in Introduction section): It is claimed that the greedy algorithm is a 2 approximation, i.e., greedy result >= 1/2 optimal result. The document where the greedy algorithm is proposed is …
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In this post, I am going to explain the code (as much as I can) from theano LSTM tutorial: http://deeplearning.net/tutorial/lstm.html You need to first understand LSTM. Here is an online recommended material: http://colah.github.io/posts/2015-08-Understanding-LSTMs/, in which many beautiful figures are provided to illustrate LSTM step by step. The tutorial aims to predict positive/negative sentiment based on movie reviews …
Twitter Sentiment Analysis: http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/ Topic classification for news (including Reuters, NewsGroup): http://disi.unitn.it/moschitti/corpora.htm Movie reviews: http://www.cs.cornell.edu/People/pabo/movie-review-data/ Other reviews: http://www.text-analytics101.com/2011/07/user-review-datasets_20.html Twitter Evaluation dataset: http://tweenator.com/index.php?page_id=13 Amazon review: https://snap.stanford.edu/data/web-Amazon.html Amazon review (upon request): https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html opinmind: https://inclass.kaggle.com/c/si650winter11/data Large movie reviews: http://ai.stanford.edu/~amaas/data/sentiment/
Use `rsync` with `-n` (dry run) so that it lists diff files but will not execute syncing. rsync -n -avcr –delete local_folder/ username@domain:remote_folder/