Machine Learning Techniques
DOWNLOAD
Download Machine Learning Techniques PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Machine Learning Techniques book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Machine Learning Techniques For Multimedia
DOWNLOAD
Author : Matthieu Cord
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-02-07
Machine Learning Techniques For Multimedia written by Matthieu Cord and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-07 with Computers categories.
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
DOWNLOAD
Author : Nietsnie Trebla
language : en
Publisher: Shelf Indulgence
Release Date :
50 Breakthrough Machine Learning Techniques In 7 Minutes Each written by Nietsnie Trebla and has been published by Shelf Indulgence this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
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
DOWNLOAD
Author : K. Gayathri Devi
language : en
Publisher: CRC Press
Release Date : 2022-05-11
Machine Learning And Deep Learning Techniques For Medical Science written by K. Gayathri Devi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-11 with Technology & Engineering categories.
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).
Machine Learning
DOWNLOAD
Author : Brian L. Taylor
language : en
Publisher:
Release Date : 2019-07-07
Machine Learning written by Brian L. Taylor and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-07 with categories.
★★★ Buy the Paperback version and then get the Kindle Book versions for FREE ★★★ Do you want to know, How to work with Robot Program? Do you want to become an expert Robot/Machine Programmer? And impress your friends with the programs you can make from scratch, Then you are on the right way and keep reading this Machine Learning book. From self-driving cars, recommendation systems to face and voice recognition, machine learning is the direction of the future. Would you choose to learn the mathematics behind machine learning to enter the fields of data analysis and artificial intelligence? There are not many resources that give detailed and straightforward examples and that go step by step through the topics of machine learning. If you are read this "Machine Learning: a quick guide to Artificial Intelligence, Neural Network and Cutting Edge Deep Learning Techniques for beginners", you are at the right place. This book not only explains what kind of mathematics is involved and the confusing notation, but also directly presents the fundamental topics of machine learning. This book will help you to learn smoothly and naturally, that will prepare you for more advanced topics besides taking away the belief that machine learning is complicated and difficult. In this book, you will attain helpful information for getting started, such as: Criteria that help distinguish tasks that are suitable for machine Supervised Machine Learning Neural Networks Unsupervised Machine Learning Learning by Reinforcement Neural Networks Neural Networks versus Conventional Computers Deep Learning Supervised Modes and Unsupervised Modes Running Python Getting Started Artificial Intelligence, Machine Learning, and Deep Learning The Future Promise of Artificial Intelligence and deep learning and more How many hours of your life are you willing to waste to gather partial or false information when you can get everything you require to REACH YOUR GOALS by reading this fantastic guide. Get Your Copy Now! Scroll Up and Click the Buy Now Button And Enjoy!
Advances On Machine And Deep Learning Techniques In Modern Strategies
DOWNLOAD
Author : Mr.Chitra Sabapathy Ranganathan
language : en
Publisher: Leilani Katie Publication
Release Date : 2024-04-02
Advances On Machine And Deep Learning Techniques In Modern Strategies written by Mr.Chitra Sabapathy Ranganathan and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-02 with Computers categories.
Mr.Chitra Sabapathy Ranganathan, Associate Vice President, Mphasis Corporation, Arizona, USA
Fundamentals Of Machine Learning Techniques
DOWNLOAD
Author : Mr. Dayakar Babu Kancherla
language : en
Publisher: Xoffencerpublication
Release Date : 2024-01-10
Fundamentals Of Machine Learning Techniques written by Mr. Dayakar Babu Kancherla and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-10 with Computers categories.
Machine learning is a subfield of computing science that evolved both from the knowledge obtained through the process of learning how to classify data based on that understanding and also from the understanding gained through the process of learning the computational-based concepts of Artificial Intelligence, or AI. Machine learning, also known as ML, is a common abbreviation for the field. To put it another way, machine learning is the process of training computers to learn on their own via their interactions with data without being explicitly taught to do so. This is accomplished through the use of artificial neural networks. Both humans and animals may claim to be the first to conceptualize what we now call learning. There are a lot of similarities to be discovered between the way that machines learn and the way animals learn. In point of fact, many of the methods that are now used in machine learning were first created to imitate the foundations of animal and human learning using computer representations. This was done to further the field of artificial intelligence. The basic scientific concept of habituation, for instance, outlines the process by which an animal progressively ceases reacting to a stimulus that has been repeatedly shown to the animal. If a dog is taught to perform a range of tasks, such as rolling over, sitting, picking up objects, etc., it is considered to be an outstanding example of animal learning since it is capable of considerable learning if it is trained to do so. If a dog is taught to execute a number of tasks, such as rolling over, sitting, picking up items, etc., it is considered to be an excellent example of animal learning. Many people believe that dogs are the best representatives of animal intelligence. As opposed to the preceding example of successful learning, there aren't many real world applications of machine learning that we can point to as evidence that it's a helpful notion in the current world. This is in contrast to the earlier demonstration of successful learning. Virtual personal assistants, traffic predictions using GPS navigation, surveillance of multiple cameras by AI to detect crime or unusual behavior of people, social media uses ML for face recognition and news feed personalization, search engine result refinement, e-mail spam filtering where a machine memorize all the previously labeled spam e-mails by the user, and a lot more applications are just some of the many places where ML is widely used. Other applications include: a lot more applications. By using all of these applications, it has become abundantly evident that making use of knowledge and experience that one already has will result in a more efficient learning process. The close link that ML has to computational statistics, which also plays a vital role, makes the process of making predictions more simpler and more straightforward. Everyone is entitled to wonder "why does a machine need to learn something?" and there is no wrong answer to this question. There are just a few compelling arguments in favor of the need of machine learning. The fact that we just said that the development of learning capabilities in robots may help us better understand how animals and people gain information should not come as a surprise to anybody.
Machine Learning For Business Analytics
DOWNLOAD
Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2023-03-08
Machine Learning For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-08 with Computers categories.
Machine Learning for Business Analytics Machine learning—also known as data mining or data analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes: A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.
Machine Learning And Deep Learning Techniques For Medical Image Recognition
DOWNLOAD
Author : Ben Othman Soufiene
language : en
Publisher: CRC Press
Release Date : 2023-12-01
Machine Learning And Deep Learning Techniques For Medical Image Recognition written by Ben Othman Soufiene and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-01 with Technology & Engineering categories.
Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.
Applied Artificial Intelligence And Machine Learning Techniques For Engineering Applications
DOWNLOAD
Author : Ravichander Janapati
language : en
Publisher: CRC Press
Release Date : 2025-08-19
Applied Artificial Intelligence And Machine Learning Techniques For Engineering Applications written by Ravichander Janapati and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-19 with Computers categories.
This book presents various machine learning applications in the field of engineering with a focus on deep learning-based machine learning approaches. It examines the relationship between three different multidisciplinary engineering branches: biomedical engineering, signal processing, and computer science. Applied Artificial Intelligence and Machine Learning Techniques for Engineering Applications explores recent advancements in the use of AI/ML in practical engineering applications by inviting top experts to share the outcomes of their most recent work. Among the topics explored are detection, measurement, and monitoring of signals (biosensors and biomedical devices) and the use of diagnostic interpretations of bioelectric data using signal-processing techniques. The authors also address several machine learning tasks, such as classification (supervised learning) and clustering (unsupervised learning), in the context of engineering. Finally, the book also describes the development of new biomaterials for use in the body. The book will be a great help to researchers and academics working in the fields of biomedical signaling and/or human-machine interface.
Machine Learning
DOWNLOAD
Author : Steven Alex
language : en
Publisher:
Release Date : 2019-11-06
Machine Learning written by Steven Alex and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-06 with categories.
★ ★ Buy the Paperback Version of this Book and Get the Kindle Book version for FREE ★ ★ Machine Learning (Update Edition 2019-2020) this Guide is a branch of artificial intelligence, This Machine Learning Series idea is relatively new. A science that researches machines to acquire new knowledge and new skills and to identify existing knowledge. The best way to understand the potential of machine learning is to explore how people and companies are currently taking advantage of it.If you are one of the almost 400 million people with machine learning worldwide, This book offers a method to Techniques! Not every machine learning model uses the same techniques, so training will depend on your approach. Let's consider a few examples: Psychology of learning Machine learning in practice Reinforcement learning Types of machine learning Learning by reinforcement Types of reinforcement The different types of learning This guidebook is going to take some time to explore machine learning, and what it is all about. There are so many different aspects of machine learning and how to make it work for your needs, and all of it is found in this guidebook. Some of the different topics that you will be able to learn about inside include: Neural networks Historical background Why use neural networks? Tasks of neural networks Deep learning Algorithms Starting with python Basic types of data Get access to free software and data sets so you can try out your very own machine learning software. See how advanced machine learning will impact our world in the future! Scroll Up and Click the Buy Now Button!