Suggested Books Today | Books - Helge Scherlund's eLearning News

Check out these books below by Springer. 

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With more than 2,900 journals and 300,000 books, Springer offers many opportunities for authors, customers and partners. 

Fundamentals of Artificial Intelligence

Fundamentals of Artificial Intelligence

Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision...

The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI.
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 Linear Algebra and Optimization for Machine Learning

Linear Algebra and Optimization for Machine Learning

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution’s manual. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 
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 Advances in Deep Learning

Advances in Deep Learning

This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.
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Robotic Musicianship - Embodied Artificial Creativity and Mechatronic Musical Expression 

Robotic Musicianship
Embodied Artificial Creativity
and Mechatronic Musical Expression

This book discusses the principles, methodologies, and challenges of robotic musicianship through an in-depth review of the work conducted at the Georgia Tech Center for Music Technology (GTCMT), where the concept was first developed. Robotic musicianship is a relatively new research field that focuses on the design and development of intelligent music-making machines. The motivation behind the field is to develop robots that not only generate music, but also collaborate with humans by listening and responding in an expressive and creative manner. This combination of human and machine creativity has the potential to surprise and inspire us to play, listen, compose, and think about music in new ways.  
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Source: Springer