| 書目名稱 | Machine and Deep Learning Algorithms and Applications | | 編輯 | Uday Shankar Shanthamallu,Andreas Spanias | | 視頻video | http://file.papertrans.cn/621/620799/620799.mp4 | | 叢書名稱 | Synthesis Lectures on Signal Processing | | 圖書封面 |  | | 描述 | This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning toaddress a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-s | | 出版日期 | Book 2022 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-03758-0 | | isbn_softcover | 978-3-031-03748-1 | | isbn_ebook | 978-3-031-03758-0Series ISSN 1932-1236 Series E-ISSN 1932-1694 | | issn_series | 1932-1236 | | copyright | Springer Nature Switzerland AG 2022 |
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