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Encompassing Artificial Intelligence techniques such as Machine Learning, Computer Vision, Natural Language Processing and many more, Vee Technologies has emerged as an Artificial intelligence development company offering elite solutions to global clients who seek technology assistance.
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This image was taken by RD Media at the RE•WORK Deep Learning in Healthcare Summit, London, 7-8 April 2016 #reworkDL
This image was taken by RD Media at the RE•WORK Deep Learning in Healthcare Summit, London, 7-8 April 2016 #reworkDL
This image was taken by RD Media at the RE•WORK Deep Learning in Healthcare Summit, London, 7-8 April 2016 #reworkDL
Understanding the Chief Data Officer – Julie Steele ODSC Boston 2015http://www.slideshare.net/odsc/julie-steele-understandingcdo As the importance of having a data strategy in place is sinking in, many organizations have added a chief data officer (CDO) to their executive team to help create and implement that strategy. www.opendatascience.com/conferences/understanding-the-chi...
This image was taken by RD Media at the RE•WORK Deep Learning in Healthcare Summit, London, 7-8 April 2016 #reworkDL
This image was taken by RD Media at the RE•WORK Deep Learning in Healthcare Summit, London, 7-8 April 2016 #reworkDL
Convolutional Neural Networks tutorial – Learn how machines interpret images
-https://data-flair.training/blogs/convolutional-neural-networks-tutorial/
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This image was taken by RD Media at the RE•WORK Deep Learning in Healthcare Summit, London, 7-8 April 2016 #reworkDL
This image was taken by RD Media at the RE•WORK Deep Learning in Healthcare Summit, London, 7-8 April 2016 #reworkDL
This image was taken by RD Media at the RE•WORK Deep Learning in Healthcare Summit, London, 7-8 April 2016 #reworkDL
Abstract
Deep learning has revolutionized many areas, including time series data mining. Multivariate time series classification (MTSC) remained to be a well-known problem in the time series data mining community, due to its availability in various practical applications such as healthcare, finance, geoscience, and bioinformatics. Recently, multivariate long short-term memory with fully convolutional network (MLSTM-FCN) and multivariate attention long short-term memory with fully convolutional network (MALSTM-FCN) have shown superior results over various state-of-the-artmethods. So, in this paper, we explore the usage of recurrent neural network (RNN), and its variants, such as bidirectional recurrent neural network (BiRNN), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and bidirectional gated recurrent unit (BiGRU). We augment these RNN variants separately by replacing long short-term memory (LSTM) in MLSTM-FCN, which is the combination of LSTM,
squeeze-and-excitation (SE) block, and fully convolutional network (FCN). Moreover, we integrate the SE block within FCN to leverage its high performance for the MTSC task. The resulting algorithms do not require heavy pre-processing or feature crafting. Thus, they could be easily deployed on real-time systems. We conduct a comprehensive evaluation with a large number of standard datasets and demonstrate that our approaches achieve notable results over the current best MTSC approach.