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Neural Networks with Model Compression

Name: Neural Networks with Model Compression
Brand: SPRINGER NATURE
SKU: 9789819950676
Price: 683.4 PLN
Availability: InStock

Autor: Tiancheng Wang, Sheng Xu

Język:

Oprawa: Twarda

Wydawca: SPRINGER NATURE

Dostępność: Dostępna u dostawcy

Wysyłamy za 10-13 dni

683.40 zł

Deep learning has achieved impressive results in image classification, computer vision and natural l...

Informacje o książce

Autor

Tiancheng Wang, Sheng Xu

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2023

strony

272

EAN

9789819950676

ISBN

9819950678

Enbook ID

43801916

Wydawca

SPRINGER NATURE

Waga

557

Pełny opis

Deep learning has achieved impressive results in image classification, computer vision and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floating-point operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, our book will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS due to its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge about machine learning and deep learning to better understand the methods described in this book.

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