Gang Xu – författare

Deep Learning: From Algorithmic Essence to Industrial Practice introduces the fundamental theories of deep learning, engineering practices, and their deployment and application in the industry. This book provides a detailed explanation of classic convolutional neural networks, recurrent neural networks, and transformer networks based on self-attention mechanisms, along with their variants, combining code demonstrations. Additionally, this book covers the applications of these models in areas including image classification, object detection, and semantic segmentation. This book also considers advancements in deep reinforcement learning and generative adversarial networks making it suitable for graduate and senior undergraduate students with backgrounds in computer science, automation, electronics, communications, mathematics, and physics, as well as professional technical personnel who wish to work or are preparing to transition into the field of artificial intelligenceThe code for book may be accessed by visiting the companion website: https://www.elsevier.com/books-and-journals/book-companion/9780443439544Provides in-depth explanations and practical code examples for the latest deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformersExamines theoretical concepts and the engineering practices required for deploying deep learning models in real-world scenariosCovers the use of distributed systems for training and deploying modelsIncludes detailed case studies and applications of deep learning models in various domains including image classification, object detection, and semantic segmentation

Deep Learning: From Algorithmic Essence to Industrial Practice introduces the fundamental theories of deep learning, engineering practices, and their deployment and application in the industry. This book provides a detailed explanation of classic convolutional neural networks, recurrent neural networks, and transformer networks based on self-attention mechanisms, along with their variants, combining code demonstrations. Additionally, this book covers the applications of these models in areas including image classification, object detection, and semantic segmentation. This book also considers advancements in deep reinforcement learning and generative adversarial networks making it suitable for graduate and senior undergraduate students with backgrounds in computer science, automation, electronics, communications, mathematics, and physics, as well as professional technical personnel who wish to work or are preparing to transition into the field of artificial intelligenceThe code for book may be accessed by visiting the companion website: https://www.elsevier.com/books-and-journals/book-companion/9780443439544- Provides in-depth explanations and practical code examples for the latest deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers- Examines theoretical concepts and the engineering practices required for deploying deep learning models in real-world scenarios- Covers the use of distributed systems for training and deploying models- Includes detailed case studies and applications of deep learning models in various domains including image classification, object detection, and semantic segmentation

The boom in tourism to and within China is quite a recent phenomenon. China is now among the leading international tourist destinations, and demand for domestic tourism is rapidly increasing. This study examines the role of tourism in the local economic development in different areas of China, and identifies the decisive factors in determining the success or failure of tourism-oriented development strategy at the local level. The author juxtaposes case studies of Guilin, a domestic and international tourist destination in a less developed local area, Suzhou, also both a domestic and international destination but in a more mature local economy, and lastly Beidaihe, a mainly domestic tourist destination.

Appendix 164 3. A 3. A. 1 Approximate Estimation of Fundamental Matrix from General Matrix 164 3. A. 2 Estimation of Affine Transformation 165 4 RECOVERY OF EPIPOLAR GEOMETRY FROM LINE SEGMENTS OR LINES 167 Line Segments or Straight Lines 168 4. 1 4. 2 Solving Motion Using Line Segments Between Two Views 173 4. 2. 1 Overlap of Two Corresponding Line Segments 173 Estimating Motion by Maximizing Overlap 175 4. 2. 2 Implementation Details 4. 2. 3 176 Reconstructing 3D Line Segments 4. 2. 4 179 4. 2. 5 Experimental Results 180 4. 2. 6 Discussions 192 4. 3 Determining Epipolar Geometry of Three Views 194 4. 3. 1 Trifocal Constraints for Point Matches 194 4. 3. 2 Trifocal Constraints for Line Correspondences 199 4. 3. 3 Linear Estimation of K, L, and M Using Points and Lines 200 4. 3. 4 Determining Camera Projection Matrices 201 4. 3. 5 Image Transfer 203 4. 4 Summary 204 5 REDEFINING STEREO, MOTION AND OBJECT RECOGNITION VIA EPIPOLAR GEOMETRY 205 5. 1 Conventional Approaches to Stereo, Motion and Object Recognition 205 5. 1. 1 Stereo 205 5. 1. 2 Motion 206 5. 1. 3 Object Recognition 207 5. 2 Correspondence in Stereo, Motion and Object Recognition as 1D Search 209 5. 2. 1 Stereo Matching 209 xi Contents 5. 2. 2 Motion Correspondence and Segmentation 209 5. 2. 3 3D Object Recognition and Localization 210 Disparity and Spatial Disparity Space 210 5.

The boom in tourism to and within China is quite a recent phenomenon. China is now among the leading international tourist destinations, and demand for domestic tourism is rapidly increasing. This study examines the role of tourism in the local economic development in different areas of China, and identifies the decisive factors in determining the success or failure of tourism-oriented development strategy at the local level. The author juxtaposes case studies of Guilin, a domestic and international tourist destination in a less developed local area, Suzhou, also both a domestic and international destination but in a more mature local economy, and lastly Beidaihe, a mainly domestic tourist destination.

The boom in tourism to and within China is quite a recent phenomenon. China is now among the leading international tourist destinations, and demand for domestic tourism is rapidly increasing. This study examines the role of tourism in the local economic development in different areas of China, and identifies the decisive factors in determining the success or failure of tourism-oriented development strategy at the local level. The author juxtaposes case studies of Guilin, a domestic and international tourist destination in a less developed local area, Suzhou, also both a domestic and international destination but in a more mature local economy, and lastly Beidaihe, a mainly domestic tourist destination.

The boom in tourism to and within China is quite a recent phenomenon. China is now among the leading international tourist destinations, and demand for domestic tourism is rapidly increasing. This study examines the role of tourism in the local economic development in different areas of China, and identifies the decisive factors in determining the success or failure of tourism-oriented development strategy at the local level. The author juxtaposes case studies of Guilin, a domestic and international tourist destination in a less developed local area, Suzhou, also both a domestic and international destination but in a more mature local economy, and lastly Beidaihe, a mainly domestic tourist destination.

Appendix 164 3. A 3. A. 1 Approximate Estimation of Fundamental Matrix from General Matrix 164 3. A. 2 Estimation of Affine Transformation 165 4 RECOVERY OF EPIPOLAR GEOMETRY FROM LINE SEGMENTS OR LINES 167 Line Segments or Straight Lines 168 4. 1 4. 2 Solving Motion Using Line Segments Between Two Views 173 4. 2. 1 Overlap of Two Corresponding Line Segments 173 Estimating Motion by Maximizing Overlap 175 4. 2. 2 Implementation Details 4. 2. 3 176 Reconstructing 3D Line Segments 4. 2. 4 179 4. 2. 5 Experimental Results 180 4. 2. 6 Discussions 192 4. 3 Determining Epipolar Geometry of Three Views 194 4. 3. 1 Trifocal Constraints for Point Matches 194 4. 3. 2 Trifocal Constraints for Line Correspondences 199 4. 3. 3 Linear Estimation of K, L, and M Using Points and Lines 200 4. 3. 4 Determining Camera Projection Matrices 201 4. 3. 5 Image Transfer 203 4. 4 Summary 204 5 REDEFINING STEREO, MOTION AND OBJECT RECOGNITION VIA EPIPOLAR GEOMETRY 205 5. 1 Conventional Approaches to Stereo, Motion and Object Recognition 205 5. 1. 1 Stereo 205 5. 1. 2 Motion 206 5. 1. 3 Object Recognition 207 5. 2 Correspondence in Stereo, Motion and Object Recognition as 1D Search 209 5. 2. 1 Stereo Matching 209 xi Contents 5. 2. 2 Motion Correspondence and Segmentation 209 5. 2. 3 3D Object Recognition and Localization 210 Disparity and Spatial Disparity Space 210 5.