Deep Learning for Perception and Recognition | Method and Applications

ISBN 10: 3725862664 / ISBN 13: 9783725862665

Published by MDPI AG, 2026

About this Item

Deep Learning for Perception and Recognition | Method and Applications | Buch | Englisch | 2026 | MDPI AG | EAN 9783725862665 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 134550254

Bibliographic Details

• Title: Deep Learning for Perception and Recognition | Method and Applications
• Publisher: MDPI AG
• Publication Date: 2026
• Language: English
• ISBN 10: 3725862664
• ISBN 13: 9783725862665
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 30D millimeters 250 millimeters width by 175 millimeters height by 30 millimeters depth
• Weight: 1174 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The rapid advancement of deep learning technology has brought about transformative breakthroughs in perception and recognition systems across a wide range of applications. In addition to driving innovation in industrial sectors, it has opened up significant opportunities in fields such as intelligent transportation, smart cities, healthcare, and robotics.

Deep learning significantly enhances the accuracy and robustness of perception and recognition systems through hierarchical feature extraction in multilayer neural networks, achieving remarkable results in areas such as soft sensing, image classification, natural language processing, and object detection. By training on large volumes of labeled data, deep learning algorithms are able to automatically learn complex feature representations and efficiently recognize objects during the perception process.

As application scenarios grow more complex and data become increasingly diverse, deep learning models continue to face significant challenges in solving real-world perception and recognition problems. These challenges include ensuring model generalization when dealing with noisy, imbalanced, or limited data; enhancing performance through self-supervised, few-shot, or transfer learning in cases of insufficient labeled data; integrating information across different scales, dimensions, and modalities.