Development and Analysis of Deep Learning ArchitecturesКНИГИ » ПРОГРАММИНГ
Название: Development and Analysis of Deep Learning Architectures Автор: Witold Pedrycz, Shyi-Ming Chen Издательство: Springer Год: 2019 (2020 Edition) Страниц: 296 Язык: английский Формат: pdf (true) Размер: 12.8 MB
This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of Deep Learning (DL) so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of Big Data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.
Ten chapters, forming this volume, are a genuine reflection of the diversity and a visible spectrum of algorithms and applications which make the underlying idea of deep learning so attractive and heavily researched nowadays. The topics covered here span a plethora of topics. Chapter “Direct Error Driven Learning for Classification in Applications Generating Big-Data” elaborates on mechanisms of learning carried out in the environment of Big Data; yet another timely topic quite visibly associated with Deep Learning. Processes of sensor design are discussed in the chapter “Deep Learning for Soft Sensor design”. The application of deep convolutional networks to healthcare is covered in the chapter “Case Study: Deep Convolutional Networks in Healthcare”. Domain adaptation for regression is presented in the chapter “Deep Domain Adaptation for Regression”. Applications to autonomous driving, speaker recognition, baby cry detection, industrial control, wireless communication, and text analysis, the chapters "Deep Learning-Based Pedestrian Detection for Automated Driving: Achievements and Future Challenges"; "Deep Learning in Speaker Recognition"; "Securing Industrial Control Systems from False Data Injection Attacks with Convolutional Neural Networks"; "Baby Cry Detection: Deep Learning and Classical Approaches"; "Deep Learning for Wireless Communications"; "Identifying Extremism in Text Using Deep Learning", are the testimony of a wealth of usages of Deep Learning.
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