TY - CHAP
T1 - Machine and Deep Learning Applications
AU - Shanthamallu, Uday Shankar
AU - Spanias, Andreas
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - In this chapter, we introduce several applications of machine learning and deep learning in different domains, including sensor and time-series, image and vision, text and natural language processing, relational data, energy, manufacturing, social media, health, security, and Internet-of-Things (IoT) applications. Until 2010, traditional ML models such as SVMs and decision trees have enjoyed successes in various tasks, including handwritten digit classification, face detection, and pattern recognition. Though traditional ML models are easy to interpret, the model’s inputs need to be well-designed, handcrafted features. On the other hand, deep learning models circumvent this problem and directly take the raw data as input and provide end-to-end learning capability. There is an unprecedented increase in machine learning and deep learning applications, especially with the emergence of fast mobile devices with access to cloud computing. While cloud computing provides the necessary computational power to train deep learning models, trained models can be easily deployed in the cloud or on embedded devices at the edge of the cloud to carry out the inference.
AB - In this chapter, we introduce several applications of machine learning and deep learning in different domains, including sensor and time-series, image and vision, text and natural language processing, relational data, energy, manufacturing, social media, health, security, and Internet-of-Things (IoT) applications. Until 2010, traditional ML models such as SVMs and decision trees have enjoyed successes in various tasks, including handwritten digit classification, face detection, and pattern recognition. Though traditional ML models are easy to interpret, the model’s inputs need to be well-designed, handcrafted features. On the other hand, deep learning models circumvent this problem and directly take the raw data as input and provide end-to-end learning capability. There is an unprecedented increase in machine learning and deep learning applications, especially with the emergence of fast mobile devices with access to cloud computing. While cloud computing provides the necessary computational power to train deep learning models, trained models can be easily deployed in the cloud or on embedded devices at the edge of the cloud to carry out the inference.
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U2 - 10.1007/978-3-031-03758-0_6
DO - 10.1007/978-3-031-03758-0_6
M3 - Chapter
AN - SCOPUS:85137805914
T3 - Studies in Computational Intelligence
SP - 59
EP - 72
BT - Studies in Computational Intelligence
PB - Springer Science and Business Media Deutschland GmbH
ER -