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[null,"1.","Record Nr.",null,"UNINA9910317796703321"]
[null,null,"Titolo",null,"Advances in plant pathology / / edited by Josphert N. Kimatu"]
[null,null,"Pubbl/distr/stampa",null,"London, England : , : IntechOpen, , [2018]"]
[{"$nulls":4},"©2018"]
[null,null,"ISBN",null,"1-83881-611-9"]
[{"$nulls":4},"1-78923-609-6"]
[null,null,"Descrizione fisica",null,"1 online resource (132 pages)"]
[null,null,"Disciplina",null,"632.3"]
[null,null,"Soggetti",null,"Plant diseases"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Plant pathology deals mainly with biotic phenomena that interfere with the normal metabolism of plants. Plants have developed mechanisms to deal with pathogenic attacks, while at the same time, pathogens are actively devising ways of overcoming the plant defense systems. Plant pathologists have been advancing their studies from morphological and physiological to now molecular studies at the gene level. There are various approaches for different microorganisms and plants. This makes the study of plant pathology diverse. This book, Advances in Plant Pathology, attempts to investigate advances in viral, fungal, bacterial, and other diagnostic molecular approaches in various plants."]
[null,"2.","Record Nr.",null,"UNINA9910157379403321"]
[null,null,"Autore",null,"Paluszek Michael"]
[null,null,"Titolo",null,"MATLAB Machine Learning / / by Michael Paluszek, Stephanie Thomas"]
[null,null,"Pubbl/distr/stampa",null,"Berkeley, CA : , : Apress : , : Imprint : Apress, , 2017"]
[null,null,"ISBN",null,"9781484222508"]
[{"$nulls":4},"1484222504"]
[null,null,"Edizione",null,"[1st ed. 2017.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XIX, 326 p. 140 illus., 74 illus. in color.)"]
[null,null,"Disciplina",null,"006"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Programming languages (Electronic computers)"]
[{"$nulls":4},"Computer programming"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Programming Languages, Compilers, Interpreters"]
[{"$nulls":4},"Programming Techniques"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references at the end of each chapters and index."]
[null,null,"Nota di contenuto",null,"1 Overview of Machine Learning -- 2 The History of Machine Learning -- 3 Software for machine learning -- 4 Representation of data for Machine Learning in MATLAB -- 5 MATLAB Graphics -- 6 Machine Learning Examples in MATLAB -- 7 Face Recognition with Deep Learning -- 8 Data Classification -- 9 Classification of Numbers Using Neural Networks -- 10 Kalman Filters -- 11 Adaptive Control -- 12 Autonomous Driving -- Bibliography."]
[null,null,null,"Sommario/riassunto","This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results. Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know "]
[{"$nulls":4},"and will learn new technology. The book then provides complete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book. What you'll learn: An overview of the field of machine learning Commercial and open source packages in MATLAB How to use MATLAB for programming and building machine learning applications MATLAB graphics for machine learning Practical real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning."]