Bhatti U. Deep Learning for Multimedia Processing Applications. Vol 2. 2024

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Bhatti U. Deep Learning for Multimedia Processing Applications. Vol 2. 2024

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Textbook in PDF format Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing. Divided into two volumes, Volume Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos. Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts. Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data. A Review on Comparative Study of Image-Denoising in Medical Imaging Remote-Sensing Image Classification: A Comprehensive Review and Applications Deep Learning Framework for Face Detection and Recognition for Dark Faces Using VGG19 with Variant of Histogram Equalization A 3D Method for Combining Geometric Verification and Volume Reconstruction in a Photo Tourism System Deep Learning Algorithms and Architectures for Multimodal Data Analysis Deep Learning Algorithms: Clustering and Classifications for Multimedia Data A Non-Reference Low-Light Image Enhancement Approach Using Deep Convolutional Neural Networks Human Pose Analysis and Gesture Recognition: Methods and Applications Human Action Recognition Using ConvLSTM with Adversarial Noise and Compressive-Sensing-Based Dimensionality Reduction, Concise and Informative Application of Machine Learning to Urban Ecology Application of Machine Learning in Urban Land Use Application of GIS and Remote-Sensing Technology in Ecosystem Services and Biodiversity Conservation From Data Quality to Model Performance: Navigating the Landscape of Deep Learning Model Evaluation Deep Learning for the Turnover Intention of Industrial Workers: Evidence from Vietnam Deep Learning for Multimedia Analysis Challenges and Techniques to Improve Deep Detection and Recognition Methods for Text Spotting Leaf Classification and Disease Detection Based on R-CCN Deep Learning Approach Multimedia Analysis with Deep Learning: Advancements & Challenges Index