Deep Learning for Image Recognition

Capture the future of computer vision with Deep Learning for Image Recognition by Peng Long and Yu Song. This authoritative guide turns complex theory into practical know-how, making it essential for students, researchers, and engineers who want to build robust image-recognition systems for real-world applications.

Start with clear, engaging explanations of convolutional neural networks, transfer learning, object detection, and semantic segmentation, then move into practical strategies for model optimization, dataset handling, and evaluation metrics. Each chapter balances mathematical rigor with intuitive examples, helping readers progress from foundational concepts to advanced architectures and state-of-the-art techniques.

Designed for global learners—from graduate students in London and San Francisco to developers in Bangalore and Shenzhen—this book emphasizes reproducible workflows and best practices for deployment across industries like healthcare, automotive, retail, and security. Expect careful discussions of common pitfalls, performance trade-offs, and how to adapt models to varied datasets and edge devices.

Whether you’re preparing for research, enhancing CV pipelines at your company, or mastering competitive benchmarks, this book gives you tools to accelerate development and improve accuracy. The writing is concise, professional, and approachable, making complex topics accessible without oversimplification.

Bring your projects to life with actionable insights from Peng Long and Yu Song. Order your copy today and join a global community pushing the boundaries of image recognition and computer vision. Available from major bookstores and online retailers worldwide.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.