Supervised and Unsupervised Data Engineering for Multimedia Data 1st Edition

Supervised and Unsupervised Data Engineering for Multimedia Data, 1st Edition by Suman Kumar Swarnkar, J. P. Patra, Sapna Singh Kshatri, Yogesh Kumar Rathore, and Tien Anh Tran is a practical, authoritative guide for engineers, researchers, and graduate students tackling modern multimedia challenges.

Grab attention with a clear promise: this book demystifies how to design robust data pipelines and apply both supervised and unsupervised techniques to image, audio, video, and sensor data. Blending theory with hands-on approaches, it walks readers from feature extraction and representation to scalable processing, model selection, and evaluation in real-world multimedia systems.

Build interest by highlighting what sets it apart: comprehensive coverage of signal preprocessing, dimensionality reduction, clustering, classification, deep learning integration, and performance optimization for big data environments. Rich case studies and regional examples make the material relevant for practitioners and academics across Asia, Europe, North America, and beyond. The text balances mathematical rigor with practical implementation notes, making complex concepts accessible without sacrificing depth.

Create desire by emphasizing outcomes: learn to construct efficient multimedia analytics pipelines, improve model accuracy with hybrid supervised/unsupervised strategies, and deploy solutions that handle heterogeneous data at scale. Ideal for data engineers, ML engineers, multimedia researchers, and technology leaders seeking actionable methods to extract insights from visual and auditory data.

Prompt action with a confident close: add Supervised and Unsupervised Data Engineering for Multimedia Data (1st Edition) to your professional library today to advance your expertise in multimedia data engineering and stay competitive in a data-driven world.

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