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Machine Learning Paradigms: Advances Deep Learning-based Technological Applications
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Machine Learning Paradigms: Advances Deep Learning-based Technological Applications in Chattanooga, TN
Current price: $169.99

Barnes and Noble
Machine Learning Paradigms: Advances Deep Learning-based Technological Applications in Chattanooga, TN
Current price: $169.99
Loading Inventory...
Size: Hardcover
At the dawn of the 4
th
Industrial Revolution, the field of
Deep Learning
(a sub-field of
Artificial Intelligence
and
Machine Learning
) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in
deep learning-based technological applications
and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1)
Deep Learning in Sensing
, (2)
Deep Learning in Social Media and IOT
, (3)
Deep Learning in the Medical Field
, (4)
Deep Learning in Systems Control
, (5) Deep Learning in Feature Vector Processing, and (6)
Evaluation of Algorithm Performance
.
This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
th
Industrial Revolution, the field of
Deep Learning
(a sub-field of
Artificial Intelligence
and
Machine Learning
) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in
deep learning-based technological applications
and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1)
Deep Learning in Sensing
, (2)
Deep Learning in Social Media and IOT
, (3)
Deep Learning in the Medical Field
, (4)
Deep Learning in Systems Control
, (5) Deep Learning in Feature Vector Processing, and (6)
Evaluation of Algorithm Performance
.
This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
At the dawn of the 4
th
Industrial Revolution, the field of
Deep Learning
(a sub-field of
Artificial Intelligence
and
Machine Learning
) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in
deep learning-based technological applications
and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1)
Deep Learning in Sensing
, (2)
Deep Learning in Social Media and IOT
, (3)
Deep Learning in the Medical Field
, (4)
Deep Learning in Systems Control
, (5) Deep Learning in Feature Vector Processing, and (6)
Evaluation of Algorithm Performance
.
This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
th
Industrial Revolution, the field of
Deep Learning
(a sub-field of
Artificial Intelligence
and
Machine Learning
) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in
deep learning-based technological applications
and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1)
Deep Learning in Sensing
, (2)
Deep Learning in Social Media and IOT
, (3)
Deep Learning in the Medical Field
, (4)
Deep Learning in Systems Control
, (5) Deep Learning in Feature Vector Processing, and (6)
Evaluation of Algorithm Performance
.
This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.

















