Buch, Englisch, Format (B × H): 152 mm x 229 mm, Gewicht: 770 g
From Algorithmic Essence to Industrial Practice
Buch, Englisch, Format (B × H): 152 mm x 229 mm, Gewicht: 770 g
ISBN: 978-0-443-43954-4
Verlag: Elsevier Science & Technology
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 intelligence
The code for book may be accessed by visiting the companion website: https://www.
elsevier.com/books-and-journals/book-companion/9780443439544
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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
The code for book may be accessed by visiting the companion website: https://www.
elsevier.com/books-and-journals/book-companion/9780443439544
