Machine Learning Techniques
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Machine Learning Techniques for Multimedia
Author: Matthieu Cord
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-02-07
Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying machine learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply. This book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in machine learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the machine learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific machine learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music. This book will be suitable for practitioners, researchers and students engaged with machine learning in multimedia applications.
50 Breakthrough Machine Learning Techniques in 7 Minutes Each
50 Breakthrough Machine Learning Techniques in 7 Minutes Each Unlock the secrets of machine learning with '50 Breakthrough Machine Learning Techniques in 7 Minutes Each', a concise and engaging guide designed for both beginners and seasoned practitioners. Dive into the revolutionary world of AI as you explore transformative concepts, tools, and methodologies that are reshaping technology and society. Each chapter is crafted to deliver essential knowledge—packed with clarity and depth—allowing you to grasp intricate techniques in mere minutes. Here are some of the captivating chapters you’ll discover: - The Rise of Deep Learning: Explore the foundations and advancements that sparked the AI revolution. - Transformers: Revolutionizing NLP: Learn how transformers have set new benchmarks in natural language processing. - Generative Adversarial Networks (GANs): Understand the mechanics behind this groundbreaking approach to data generation. - Reinforcement Learning in Gaming: Find out how AI is transforming gaming experiences through intelligent behavior. - AutoML: Automating the Machine Learning Pipeline: Discover how automation is simplifying the ML workflow. - Neural Architecture Search: Delve into techniques that optimize model design through smart search algorithms. - Federated Learning: Privacy-Preserving AI: Examine how distributed learning models maintain data privacy while training algorithms. - Explainable AI (XAI): Learn about the importance of transparency in AI decision-making. - Few-Shot and Zero-Shot Learning: Understand approaches that enable models to learn with minimal data. - Transfer Learning for Better Performance: Explore the power of leveraging existing knowledge across tasks. - Graph Neural Networks: Get acquainted with this innovative technique for processing graph-structured data. - Quantum Machine Learning: Discover the potential of quantum computing in advancing machine learning. - Neuro-Symbolic AI: Investigate the integration of neural networks with symbolic reasoning. - Self-Supervised Learning: Learn about learning without labeled data and its growing significance. - Contrastive Learning: Understand this emerging framework for representation learning. - Meta-Learning: Learning to Learn: Delve into techniques that enable algorithms to adapt quickly. - Hyperparameter Optimization: Master the art of fine-tuning models for peak performance. - Data Augmentation Techniques: Enhance your datasets to improve model robustness. - Sequence-to-Sequence Models: Explore architectures suited for sequence prediction tasks. - Attention Mechanisms: Uncover the secret behind focused learning processes in neural networks. - Multi-Modal Learning: Investigate how combining multiple data types can improve results. - Ethics in Machine Learning: Engage with the critical conversations around responsible AI. - Robustness and Adversarial Attack Defense: Learn how to build resilient machine learning systems. - Computer Vision Advances with CNNs: Discover the state-of-the-art techniques in image processing. - Time Series Forecasting with LSTM: Master the application of LSTM networks for sequential data. - Federated Transfer Learning: Explore models that generalize across distributed datasets. - Embedding Techniques: Word2Vec and Beyond: Understand how to represent words in vector space. - Machine Learning for Drug Discovery: Learn how AI is revolutionizing the pharmaceutical industry. - AI in Financial Predictive Analytics: Discover applications of machine learning in finance. - Natural Language Processing with BERT: Grasp the impact of BERT on modern NLP tasks. - Sparse Learning Approaches: Delve into techniques that reduce model complexity while maintaining performance. - Incremental Learning Approaches: Understand how models can learn over time with new data. - AI for Climate Modeling: Explore how machine learning contributes to environmental science. - Evolved Neural Networks: Investigate the future of architecture design through evolutionary principles. - Ensemble Learning Techniques: Learn about combining multiple models for improved accuracy. - Interactive AI: Human-in-the-Loop Systems: Discover how human feedback enhances AI performance. - Causal Inference with Machine Learning: Understand the techniques used to identify causal relationships. - Robotic Process Automation for Social Good: Explore how AI can streamline processes that benefit society. - Recommender Systems Evolution: Learn about the advancements that personalize user experiences. - Blockchain and Machine Learning Synergy: Investigate the intersection of these two groundbreaking technologies. - Edge AI for Real-Time Decision Making: Discover how AI is deployed closer to data sources for instant analysis. - Energy-Efficient Machine Learning: Engage with techniques that reduce the carbon footprint of AI. - Augmented Reality and ML Integration: Understand how machine learning enhances AR experiences. - Voice and Speech Recognition Advances: Explore the latest breakthroughs in human-computer interaction. - ML in Cybersecurity: Learn about the critical role of AI in defending against cyber threats. - Flight Data Analysis with AI: Discover how machine learning optimizes aviation safety and efficiency. - Healthcare Diagnostics through ML: Understand how AI is transforming medical diagnostics and decision-making. - AI-Driven Creative Applications: Explore the intersection of art and AI in the creative process. Whether you’re a student, a professional, or simply curious about machine learning, this book provides a digestible approach to mastering key techniques that will shape the future of technology. Join the revolution and elevate your understanding of AI in just seven minutes at a time!
Machine Learning and Deep Learning Techniques for Medical Science
The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis. The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images. This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector. Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines DL theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India. Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India. Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).