Innovations in Machine Learning

Theory and Applications (Studies in Fuzziness and Soft Computing)

Publisher: Springer

Written in English
Cover of: Innovations in Machine Learning |
Published: Pages: 274 Downloads: 861
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  • Machine learning,
  • Artificial intelligence,
  • Computers,
  • Mathematics,
  • Computer Books: General,
  • Applied,
  • Artificial Intelligence - General,
  • Case-based Machine Learning Paradigms,
  • Computational Algorithms in Machine Learning,
  • Explanation-based Learning Paradigms,
  • Mathematics / Applied

Edition Notes

ContributionsDawn E. Holmes (Editor), Lakhmi C. Jain (Editor)
The Physical Object
Number of Pages274
ID Numbers
Open LibraryOL9056131M
ISBN 103540306099
ISBN 109783540306092

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Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field.

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In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field.

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This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained.

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Machine Learning is more approachable than ever before and the number of companies applying Machine Learning to build AI powered applications and products has dramatically increased in recent years. On this journey of adopting Machine Learning, many companies learn successful Machine Learning projects require good software infrastructure to.

Machine Learning for Absolute Beginners has been written and designed for absolute beginners. It opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing specific algorithms applied in machine learning, including their pros and cons.

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GANs in Action teaches you how to build and train your own Generative Adversarial Networks, one of the most important innovations in deep learning. In this book, you'll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator networks.

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Machine learning – as well as deep learning, natural language processing and cognitive computing – are driving innovations in identifying images.