Neural Networks with Model Compression (1st ed. 2024) (Computational Intelligence Methods and Applications)
By: and and and
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- Synopsis
- 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.
- Copyright:
- 2024
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9789819950683
- Related ISBNs:
- 9789819950676
- Publisher:
- Springer Nature Singapore
- Date of Addition:
- 03/08/24
- Copyrighted By:
- The Editor
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Art and Architecture, Computers and Internet
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
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