View allAll Photos Tagged DeepLearning
Convolutional Neural Networks tutorial – Learn how machines interpret images
-https://data-flair.training/blogs/convolutional-neural-networks-tutorial/
The AI solutions offered by Centurysoft empowers you with business growth by minimizing your labor and infrastructure cost. We have years of experience in analyzing customers requirement and delivering industry best applications to meet their business needs. Lets, Explore our services.
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
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.
The Art, Technology, and Culture Colloquium with artist and engineer Mike Tyka in conversation with Gray Area Executive Director Josette Melchor on Deep Dreams: Between Inspiration and Hallucination.
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
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