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Implementations And Applications Of Machine Learning
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Book Synopsis Handbook of Research on Applications and Implementations of Machine Learning Techniques by : Sathiyamoorthi Velayutham
Download or read book Handbook of Research on Applications and Implementations of Machine Learning Techniques written by Sathiyamoorthi Velayutham and published by IGI Global, Engineering Science Reference. This book was released on 2019-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book examines the practical applications and implementation of various machine learning techniques in various fields such as agriculture, medical, image processing, and networking"--
Book Synopsis Challenges and Applications for Implementing Machine Learning in Computer Vision by : Kashyap, Ramgopal
Download or read book Challenges and Applications for Implementing Machine Learning in Computer Vision written by Kashyap, Ramgopal and published by IGI Global. This book was released on 2019-10-04 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
Book Synopsis Implementations and Applications of Machine Learning by : Saad Subair
Download or read book Implementations and Applications of Machine Learning written by Saad Subair and published by Springer Nature. This book was released on 2020-04-23 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book’s GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network and power systems optimization; computer vision; image and facial recognition; protein prediction; data mining; and data discovery. Numerous state-of-the-art machine learning techniques are employed (with detailed explanations), including biologically-inspired optimization (genetic and other evolutionary algorithms, swarm intelligence); Viola Jones face detection; Gaussian mixture modeling; support vector machines; deep convolutional neural networks with performance enhancement techniques (including network design, learning rate optimization, data augmentation, transfer learning); spiking neural networks and timing dependent plasticity; frequent itemset mining; binary classification; and dynamic programming. This book provides valuable information on effective, cutting-edge techniques, and approaches for students, researchers, practitioners, and teachers in the field of machine learning.
Book Synopsis Deep Learning Applications, Volume 2 by : M. Arif Wani
Download or read book Deep Learning Applications, Volume 2 written by M. Arif Wani and published by Springer. This book was released on 2020-12-14 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.
Book Synopsis Applications of Machine Learning by : Prashant Johri
Download or read book Applications of Machine Learning written by Prashant Johri and published by Springer Nature. This book was released on 2020-05-04 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.
Book Synopsis Proceedings of the Second International Conference on Information Management and Machine Intelligence by : Dinesh Goyal
Download or read book Proceedings of the Second International Conference on Information Management and Machine Intelligence written by Dinesh Goyal and published by Springer Nature. This book was released on 2021-01-22 with total page 751 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features selected papers presented at Second International Conference on International Conference on Information Management & Machine Intelligence (ICIMMI 2020) held at Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India during 24 – 25 July 2020. It covers a range of topics, including data analytics; AI; machine and deep learning; information management, security, processing techniques and interpretation; applications of artificial intelligence in soft computing and pattern recognition; cloud-based applications for machine learning; application of IoT in power distribution systems; as well as wireless sensor networks and adaptive wireless communication.
Book Synopsis VLSI and Hardware Implementations using Modern Machine Learning Methods by : Sandeep Saini
Download or read book VLSI and Hardware Implementations using Modern Machine Learning Methods written by Sandeep Saini and published by CRC Press. This book was released on 2021-12-30 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides the details of state-of-the-art machine learning methods used in VLSI Design. Discusses hardware implementation and device modeling pertaining to machine learning algorithms. Explores machine learning for various VLSI architectures and reconfigurable computing. Illustrate latest techniques for device size and feature optimization. Highlight latest case studies and reviews of the methods used for hardware implementation.
Book Synopsis Machine Learning by : Andrea Mechelli
Download or read book Machine Learning written by Andrea Mechelli and published by Academic Press. This book was released on 2019-11-14 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. Provides a non-technical introduction to machine learning and applications to brain disorders Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches Covers the main methodological challenges in the application of machine learning to brain disorders Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python
Book Synopsis Handbook of Research on Applications and Implementations of Machine Learning Techniques by : Sathiyamoorthi Velayutham
Download or read book Handbook of Research on Applications and Implementations of Machine Learning Techniques written by Sathiyamoorthi Velayutham and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Handbook of Research on Emerging Trends and Applications of Machine Learning by : Solanki, Arun
Download or read book Handbook of Research on Emerging Trends and Applications of Machine Learning written by Solanki, Arun and published by IGI Global. This book was released on 2019-12-13 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.
Book Synopsis Pro Machine Learning Algorithms by : V Kishore Ayyadevara
Download or read book Pro Machine Learning Algorithms written by V Kishore Ayyadevara and published by Apress. This book was released on 2018-06-30 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. What You Will Learn Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning Who This Book Is For Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.
Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Book Synopsis Building Machine Learning Powered Applications by : Emmanuel Ameisen
Download or read book Building Machine Learning Powered Applications written by Emmanuel Ameisen and published by "O'Reilly Media, Inc.". This book was released on 2020-01-21 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you: Define your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML models and address performance bottlenecks Deploy and monitor your models in a production environment
Book Synopsis Machine Learning Algorithms and Applications by : Mettu Srinivas
Download or read book Machine Learning Algorithms and Applications written by Mettu Srinivas and published by John Wiley & Sons. This book was released on 2021-08-10 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.
Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard
Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Book Synopsis Machine Learning and Deep Learning in Real-Time Applications by : Mahrishi, Mehul
Download or read book Machine Learning and Deep Learning in Real-Time Applications written by Mahrishi, Mehul and published by IGI Global. This book was released on 2020-04-24 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.
Book Synopsis Explainable Machine Learning Models and Architectures by : Suman Lata Tripathi
Download or read book Explainable Machine Learning Models and Architectures written by Suman Lata Tripathi and published by John Wiley & Sons. This book was released on 2023-10-03 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: EXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURES This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications. Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have a lot of scope for improvements in terms of accuracy, speed, computational powers, and overall power consumption. This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.
