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. It 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, it covers the applications of these models in areas such as image classification, object detection, semantic segmentation, etc. The book also considers the advancements in deep reinforcement learning and generative adversarial networks.
Table of Contents
1. Neural Networks2. Convolutional Neural Networks Image Classification and Object Detection
3. Convolutional Neural Networks Semantic Segmentation
4. Recurrent Neural Networks
5. Distributed Deep Learning Systems
6. Frontiers of Deep Learning
7. Special Lectures
8. Transformer and Its Companions
9. Core Practices
10. Deep Learning Inference Systems
Authors
Shuhao Wang Tsinghua University, China.Dr Shuhao Wang received his from Tsinghua University; he is a fellow at the Institute for Interdisciplinary Information Sciences at Tsinghua University and is currently the co-founder and CTO of 'Thorough Future.' He has conducted research on data science and artificial intelligence at Baidu, NovuMind, and JD.com. He holds over 20 national patents.
Dr. Wang has received several key accolades, such as the "30 New Generation Digital Economy Talents" award at the 2019 Wuzhen Internet Summit and the Year 2022 Fall Asia-Pacific Signal and Information Processing Association Industrial Distinguished Leaders award, and was named one of Alibaba Cloud's "Seeing New Power" figures of 2022 Gang Xu Fudan University, PR China. Dr Gang Xu gained his Ph.D. from Tsinghua University; he is currently an Assistant Professor at the Institute of Complex Systems Multiscale Research at Fudan University. Dr. Xu's primary research focuses on the application of artificial intelligence in medical imaging and computational biology
