Deep Learning for Engineers

Deep Learning for Engineers provides a comprehensive guide to understanding and applying deep learning models, featuring an easy-to-understand coding structure with Python and PyTorch and an in-depth explanation of four case studies.

Format: Paperback / softback
Length: 158 pages
Publication date: 28 February 2024
Publisher: Taylor & Francis Ltd

Deep Learning for Engineers is a comprehensive guide that delves into the realm of deep learning, providing a foundational understanding of its principles and essential components. Designed as a practical resource for applying deep learning models in real-world scenarios, this book offers a user-friendly coding structure using Python and PyTorch, accompanied by detailed explanations of four typical deep learning case studies: image classification, object detection, semantic segmentation, and image captioning.

In addition to covering the theoretical foundations, the book explores the architecture of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), discussing their practical implementations in various scientific and engineering fields. It also includes comprehensive exercise problems for each case study, focusing on various fine-tuning approaches in deep learning. Whether you are a science and engineering student, an academic researcher, or a professional in the industry, this book offers valuable insights and practical knowledge to help you harness the power of deep learning.

Weight: 310g
Dimension: 156 x 234 x 13 (mm)
ISBN-13: 9781032515816