Neural Networks with Model Compression by Baochang Zhang (.PDF)+

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File Size: 34.3 MB

Neural Networks with Model Compression by Baochang Zhang, Tiancheng Wang, Sheng Xu, David Doermann
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Overview: Deep Learning has achieved impressive results in image classification, Computer Vision and Natural Language Processing (NLP). 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. Deep Learning is a subset of Machine Learning that focuses on developing and applying artificial neural networks with multiple layers, also known as deep neural networks. It is inspired by the structure and function of the human brain, specifically the interconnectedness of neurons. Deep Learning models can have many architectures, depending on the task and data being addressed. Common architectures include feedforward neural networks, convolutional neural networks (CNNs) for image analysis, recurrent neural networks (RNNs) for sequence data, and transformers for natural language processing (NLP) tasks. Deep Learning has revolutionized the field of Artificial Intelligence, enabling machines to learn and make intelligent decisions from vast amounts of data.
Genre: Non-Fiction > Tech & Devices

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