Machine Learning Algorithms for Signal and Image Processing
-
- Englisch ausgewählt
181,99 €
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Gebundene Ausgabe
Erscheinungsdatum
08.12.2022
Herausgeber
Deepika Ghai + weitereVerlag
John Wiley & SonsSeitenzahl
512
Maße (L/B/H)
26/20,8/3,2 cm
Gewicht
1298 g
Auflage
1. Auflage
Sprache
Englisch
ISBN
978-1-119-86182-9
Machine Learning Algorithms for Signal and Image Processing
Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing
Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks.
Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as:
* Speech recognition, image reconstruction, object classification and detection, and text processing
* Healthcare monitoring, biomedical systems, and green energy
* How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time
* Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection
Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.
Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing
Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks.
Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as:
* Speech recognition, image reconstruction, object classification and detection, and text processing
* Healthcare monitoring, biomedical systems, and green energy
* How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time
* Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection
Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.
Kundinnen und Kunden meinen
Verfassen Sie die erste Bewertung zu diesem Artikel
Helfen Sie anderen Kund*innen durch Ihre Meinung
Kurze Frage zu unserer Seite
Vielen Dank für Ihr Feedback
Wir nutzen Ihr Feedback, um unsere Produktseiten zu verbessern. Bitte haben Sie Verständnis, dass wir Ihnen keine Rückmeldung geben können. Falls Sie Kontakt mit uns aufnehmen möchten, können Sie sich aber gerne an unseren Kund*innenservice wenden.
zum Kundenservice