Shuhao Wang, Gang Xu

Deep Learning

From Algorithmic Essence to Industrial Practice

• Broschiertes Buch

Produktbeschreibung

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.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 250
• Erscheinungstermin: 1. Juli 2025
• Englisch
• ISBN-13: 9780443439544
• ISBN-10: 0443439540
• Artikelnr.: 73490657

Autorenporträt

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

Inhaltsangabe

1. Neural Networks
2. 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