Hallo! Tracked shipping to Netherlands with Delivery Duty Paid for just €7 

Ship to
Netherlands
0
  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

Select your country

Americas

Europe

Rest of the world

portada Deep Learning for Perception and Recognition: Method and Applications
Type
Physical Book
Publisher
Language
English
Pages
390
Format
Hardcover
Dimensions
24.4x17x3 cm
ISBN13
9783725862665

Deep Learning for Perception and Recognition: Method and Applications

Wu, Gaochang; Fan, Zizhu; Pan, Dong (Author) · Mdpi AG · Hardcover

Deep Learning for Perception and Recognition: Method and Applications - Wu, Gaochang; Fan, Zizhu; Pan, Dong

New Book Imported to Netherlands
Delivery: 27 Aug - 01 Sep Shipping: 4 to 5 business days.
€ 108,37
Import costs and 9% BTW included in the price ✅
€ 108,37

Synopsis "Deep Learning for Perception and Recognition: Method and Applications"

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.

Customers reviews

Frequently Asked Questions about the Book

All books in our catalog are Original.
The book is written in English.
The binding of this edition is Hardcover.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews