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Deep Learning for Data Analytics - Foundations, Biomedical Applications, and Challenges (Paperback)
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Deep Learning for Data Analytics - Foundations, Biomedical Applications, and Challenges (Paperback)
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Deep learning, a branch of Artificial Intelligence and machine
learning, has led to new approaches to solving problems in a
variety of domains including data science, data analytics and
biomedical engineering. Deep Learning for Data Analytics:
Foundations, Biomedical Applications and Challenges provides
readers with a focused approach for the design and implementation
of deep learning concepts using data analytics techniques in large
scale environments. Deep learning algorithms are based on
artificial neural network models to cascade multiple layers of
nonlinear processing, which aids in feature extraction and learning
in supervised and unsupervised ways, including classification and
pattern analysis. Deep learning transforms data through a cascade
of layers, helping systems analyze and process complex data sets.
Deep learning algorithms extract high level complex data and
process these complex sets to relatively simpler ideas formulated
in the preceding level of the hierarchy. The authors of this book
focus on suitable data analytics methods to solve complex real
world problems such as medical image recognition, biomedical
engineering, and object tracking using deep learning methodologies.
The book provides a pragmatic direction for researchers who wish to
analyze large volumes of data for business, engineering, and
biomedical applications. Deep learning architectures including deep
neural networks, recurrent neural networks, and deep belief
networks can be used to help resolve problems in applications such
as natural language processing, speech recognition, computer
vision, bioinoformatics, audio recognition, drug design, and
medical image analysis.
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