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A concise introduction to machine learning / A C Fau
Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019
xix, 314 pages : illustrations ; 24 cm.
Ký hiệu phân loại (DDC): 006.31
"Machine Learning is known by many different names, and is used in many areas of science. It is also used for a variety of applications, including spam filtering, optical character recognition, search engines, computer vision, NLP, advertising, fraud detection, robotics, data prediction, astronomy. Considering this, it can often be difficult to find a solution to a problem in the literature, simply because different words and phrases are used for the same concept. This class-tested textbook aims to alleviate this, using mathematics as the common language. It covers a variety of machine learning concepts from basic principles, and llustrates every concept using examples in MATLAB"
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2
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AI and Neurology / Julian Bösel, Rohan Mathur, Lin Cheng, Marianna S. Varelas, Markus A. Hobert & José I. Suarez // Neurological Research and Practice. - . - . - ISSN: 2524-3489
9 pages
Ký hiệu phân loại (DDC): 610.28
Background Artificial Intelligence is influencing medicine on all levels. Neurology, one of the most complex and progressive medical disciplines, is no exception. No longer limited to neuroimaging, where data-driven approaches were initiated, machine and deep learning methodologies are taking neurologic diagnostics, prognostication, predictions, decision making and even therapy to very promising potentials.
Main body In this review, the basic principles of different types of Artificial Intelligence and the options to apply them to neurology are summarized. Examples of noteworthy studies on such applications are presented from the fields of acute and intensive care neurology, stroke, epilepsy, and movement disorders. Finally, these potentials are matched with risks and challenges jeopardizing ethics, safety and equality, that need to be heeded by neurologists welcoming Artificial Intelligence to their field of expertise.
Conclusion Artificial intelligence is and will be changing neurology. Studies need to be taken to the prospective level and algorithms undergo federated learning to reach generalizability. Neurologists need to master not only the benefits but also the risks in safety, ethics and equity of such data-driven form of medicine.
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3
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An Introduction to Machine Learning / Miroslav Kubat
Cham : Springer, 2017
348 p. ; cm.
Ký hiệu phân loại (DDC): 006.3
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of "boosting," how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
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4
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Apache Spark deep learning cookbook : over 80 recipes that streamline deep learning in a distributed environment with apache spark / Ahmed Sherif, Amrith Ravindra
Birmingham, UK : Packt Publishing, 2018
462 pages. : illustrations ; 24 cm.
Ký hiệu phân loại (DDC): 006.31
With the help of the Apache Spark Deep Learning Cookbook, you'll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you'll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you'll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras
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5
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