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Machine Vision Algorithms In Java
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Book Synopsis Machine Vision Algorithms in Java by : Paul F. Whelan
Download or read book Machine Vision Algorithms in Java written by Paul F. Whelan and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents key machine vision techniques and algorithms, along with the associated Java source code. Special features include a complete self-contained treatment of all topics and techniques essential to the understanding and implementation of machine vision; an introduction to object-oriented programming and to the Java programming language, with particular reference to its imaging capabilities; Java source code for a wide range of real-world image processing and analysis functions; an introduction to the Java 2D imaging and Java Advanced Imaging (JAI) API; and a wide range of illustrative examples.
Book Synopsis Hands-On Java Deep Learning for Computer Vision by : Klevis Ramo
Download or read book Hands-On Java Deep Learning for Computer Vision written by Klevis Ramo and published by Packt Publishing Ltd. This book was released on 2019-02-21 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of Java and deep learning to build production-grade Computer Vision applications Key FeaturesBuild real-world Computer Vision applications using the power of neural networks Implement image classification, object detection, and face recognitionKnow best practices on effectively building and deploying deep learning models in JavaBook Description Although machine learning is an exciting world to explore, you may feel confused by all of its theoretical aspects. As a Java developer, you will be used to telling the computer exactly what to do, instead of being shown how data is generated; this causes many developers to struggle to adapt to machine learning. The goal of this book is to walk you through the process of efficiently training machine learning and deep learning models for Computer Vision using the most up-to-date techniques. The book is designed to familiarize you with neural networks, enabling you to train them efficiently, customize existing state-of-the-art architectures, build real-world Java applications, and get great results in a short space of time. You will build real-world Computer Vision applications, ranging from a simple Java handwritten digit recognition model to real-time Java autonomous car driving systems and face recognition models. By the end of this book, you will have mastered the best practices and modern techniques needed to build advanced Computer Vision Java applications and achieve production-grade accuracy. What you will learnDiscover neural networks and their applications in Computer VisionExplore the popular Java frameworks and libraries for deep learningBuild deep neural networks in Java Implement an end-to-end image classification application in JavaPerform real-time video object detection using deep learningEnhance performance and deploy applications for productionWho this book is for This book is for data scientists, machine learning developers and deep learning practitioners with Java knowledge who want to implement machine learning and deep neural networks in the computer vision domain. You will need to have a basic knowledge of Java programming.
Book Synopsis Practical Java Machine Learning by : Mark Wickham
Download or read book Practical Java Machine Learning written by Mark Wickham and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You will be introduced to tools which can help you identify and manage your data including JSON, visualization, NoSQL databases, and cloud platforms including Google Cloud Platform and Amazon Web Services. Practical Java Machine Learning includes multiple projects, with particular focus on the Android mobile platform and features such as sensors, camera, and connectivity, each of which produce data that can power unique machine learning solutions. You will learn to build a variety of applications that demonstrate the capabilities of the Google Cloud Platform machine learning API, including data visualization for Java; document classification using the Weka ML environment; audio file classification for Android using ML with spectrogram voice data; and machine learning using device sensor data. After reading this book, you will come away with case study examples and projects that you can take away as templates for re-use and exploration for your own machine learning programming projects with Java. You will: Identify, organize, and architect the data required for ML projects Deploy ML solutions in conjunction with cloud providers such as Google and Amazon Determine which algorithm is the most appropriate for a specific ML problem Implement Java ML solutions on Android mobile devices Create Java ML solutions to work with sensor data Build Java streaming based solutions.
Book Synopsis Machine Vision by : Wesley E. Snyder
Download or read book Machine Vision written by Wesley E. Snyder and published by Cambridge University Press. This book was released on 2010-11-25 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 2004 book is an accessible and comprehensive introduction to machine vision. It provides all the necessary theoretical tools and shows how they are applied in actual image processing and machine vision systems. A key feature is the inclusion of many programming exercises that give insights into the development of practical image processing algorithms. The authors begin with a review of mathematical principles and go on to discuss key issues in image processing such as the description and characterization of images, edge detection, restoration and feature extraction, segmentation, texture and shape. They also discuss image matching, statistical pattern recognition, clustering, and syntactic pattern recognition. Important applications are described, including optical character recognition and automatic target recognition. Software and data used in the book can be found at www.cambridge.org/9780521830461. A useful reference for practitioners, the book is aimed at graduate students in electrical engineering, computer science and mathematics.
Download or read book Machine Vision written by Herbert Freeman and published by Elsevier. This book was released on 2012-12-02 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Vision: Algorithms, Architectures, and Systems contains the proceedings of the workshop ""Machine Vision: Where Are We and Where Are We Going?"" sponsored by the Center for Computer Aids for Industrial Productivity (CAIP) at Rutgers University and held in April 1987 in New Brunswick, New Jersey. The papers review the state of the art of machine vision and sets directions for future research. Topics covered include ""smart sensing"" in machine vision, computer architectures for machine vision, and range image segmentation. Comprised of 14 chapters, this book opens with an overview of ""smart sensing"" strategies in machine vision and illustrates how smart sensing may fit into a general purpose vision system by implementing a flexible, modular system called Pipeline Pyramid Machine. The discussion then turns to a hierarchy of local autonomy for processor arrays, focusing on the progression from pure SIMD to complete MIMD as well as the hardware penalties that arise when autonomy is increased. The following chapters explore schemes for integrating vision modules on fine-grained machines; computer architectures for real-time machine vision systems; the application of machine vision to industrial inspection; and characteristics of technologies and social processes that are inhibiting the development and/or evolution of machine vision. Machine vision research at General Motors is also considered. The final chapter assesses future prospects for machine vision and highlights directions for research. This monograph will be a useful resource for practitioners in the fields of computer science and applied mathematics.
Book Synopsis Digital Image Processing by : Wilhelm Burger
Download or read book Digital Image Processing written by Wilhelm Burger and published by Springer Science & Business Media. This book was released on 2012-01-19 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written as an introduction for undergraduate students, this textbook covers the most important methods in digital image processing. Formal and mathematical aspects are discussed at a fundamental level and various practical examples and exercises supplement the text. The book uses the image processing environment ImageJ, freely distributed by the National Institute of Health. A comprehensive website supports the book, and contains full source code for all examples in the book, a question and answer forum, slides for instructors, etc. Digital Image Processing in Java is the definitive textbook for computer science students studying image processing and digital processing.
Book Synopsis Programming Computer Vision with Python by : Jan Erik Solem
Download or read book Programming Computer Vision with Python written by Jan Erik Solem and published by "O'Reilly Media, Inc.". This book was released on 2012-06-19 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python. Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills. Learn techniques used in robot navigation, medical image analysis, and other computer vision applications Work with image mappings and transforms, such as texture warping and panorama creation Compute 3D reconstructions from several images of the same scene Organize images based on similarity or content, using clustering methods Build efficient image retrieval techniques to search for images based on visual content Use algorithms to classify image content and recognize objects Access the popular OpenCV library through a Python interface
Book Synopsis Machine Learning in Java by : Bostjan Kaluza
Download or read book Machine Learning in Java written by Bostjan Kaluza and published by . This book was released on 2016-04-29 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design, build, and deploy your own machine learning applications by leveraging key Java machine learning librariesAbout This Book- Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries- Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applications- Packed with practical advice and tips to help you get to grips with applied machine learningWho This Book Is ForIf you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life. You should be familiar with Java programming and data mining concepts to make the most of this book, but no prior experience with data mining packages is necessary.What You Will Learn- Understand the basic steps of applied machine learning and how to differentiate among various machine learning approaches- Discover key Java machine learning libraries, what each library brings to the table, and what kind of problems each are able to solve- Learn how to implement classification, regression, and clustering- Develop a sustainable strategy for customer retention by predicting likely churn candidates- Build a scalable recommendation engine with Apache Mahout- Apply machine learning to fraud, anomaly, and outlier detection- Experiment with deep learning concepts, algorithms, and the toolbox for deep learning- Write your own activity recognition model for eHealth applications using mobile sensorsIn DetailAs the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering.Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will explore related web resources and technologies that will help you take your learning to the next level.By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.Style and approachThis is a practical tutorial that uses hands-on examples to step through some real-world applications of machine learning. Without shying away from the technical details, you will explore machine learning with Java libraries using clear and practical examples. You will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.
Book Synopsis Machine Learning in Java by : AshishSingh Bhatia
Download or read book Machine Learning in Java written by AshishSingh Bhatia and published by Packt Publishing Ltd. This book was released on 2018-11-28 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of Java and its associated machine learning libraries to build powerful predictive models Key FeaturesSolve predictive modeling problems using the most popular machine learning Java libraries Explore data processing, machine learning, and NLP concepts using JavaML, WEKA, MALLET librariesPractical examples, tips, and tricks to help you understand applied machine learning in JavaBook Description As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this book works for JDK 8 and above, the code is tested on JDK 11. Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will have explored related web resources and technologies that will help you take your learning to the next level. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. What you will learnDiscover key Java machine learning librariesImplement concepts such as classification, regression, and clusteringDevelop a customer retention strategy by predicting likely churn candidatesBuild a scalable recommendation engine with Apache MahoutApply machine learning to fraud, anomaly, and outlier detectionExperiment with deep learning concepts and algorithmsWrite your own activity recognition model for eHealth applicationsWho this book is for If you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications with ease. You should be familiar with Java programming and some basic data mining concepts to make the most of this book, but no prior experience with machine learning is required.
Book Synopsis Algorithms for Image Processing and Computer Vision by : J. R. Parker
Download or read book Algorithms for Image Processing and Computer Vision written by J. R. Parker and published by John Wiley & Sons. This book was released on 2010-11-29 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: A cookbook of algorithms for common image processing applications Thanks to advances in computer hardware and software, algorithms have been developed that support sophisticated image processing without requiring an extensive background in mathematics. This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids. It’s an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists who require highly specialized image processing. Algorithms now exist for a wide variety of sophisticated image processing applications required by software engineers and developers, advanced programmers, graphics programmers, scientists, and related specialists This bestselling book has been completely updated to include the latest algorithms, including 2D vision methods in content-based searches, details on modern classifier methods, and graphics cards used as image processing computational aids Saves hours of mathematical calculating by using distributed processing and GPU programming, and gives non-mathematicians the shortcuts needed to program relatively sophisticated applications. Algorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications.
Book Synopsis Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms by : Sandeep Kumar
Download or read book Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms written by Sandeep Kumar and published by John Wiley & Sons. This book was released on 2021-11-23 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: COGNITIVE BEHAVIOR AND HUMAN COMPUTER INTERACTION BASED ON MACHINE LEARNING ALGORITHMS The objective of this book is to provide the most relevant information on Human-Computer Interaction to academics, researchers, and students and for those from industry who wish to know more about the real-time application of user interface design. Human-computer interaction (HCI) is the academic discipline, which most of us think of as UI design, that focuses on how human beings and computers interact at ever-increasing levels of both complexity and simplicity. Because of the importance of the subject, this book aims to provide more relevant information that will be useful to students, academics, and researchers in the industry who wish to know more about its real-time application. In addition to providing content on theory, cognition, design, evaluation, and user diversity, this book also explains the underlying causes of the cognitive, social and organizational problems typically devoted to descriptions of rehabilitation methods for specific cognitive processes. Also described are the new modeling algorithms accessible to cognitive scientists from a variety of different areas. This book is inherently interdisciplinary and contains original research in computing, engineering, artificial intelligence, psychology, linguistics, and social and system organization as applied to the design, implementation, application, analysis, and evaluation of interactive systems. Since machine learning research has already been carried out for a decade in various applications, the new learning approach is mainly used in machine learning-based cognitive applications. Since this will direct the future research of scientists and researchers working in neuroscience, neuroimaging, machine learning-based brain mapping, and modeling, etc., this book highlights the framework of a novel robust method for advanced cross-industry HCI technologies. These implementation strategies and future research directions will meet the design and application requirements of several modern and real-time applications for a long time to come. Audience: A wide range of researchers, industry practitioners, and students will be interested in this book including those in artificial intelligence, machine learning, cognition, computer programming and engineering, as well as social sciences such as psychology and linguistics.
Book Synopsis OpenCV 3.0 Computer Vision with Java by : Daniel Lélis Baggio
Download or read book OpenCV 3.0 Computer Vision with Java written by Daniel Lélis Baggio and published by Packt Publishing Ltd. This book was released on 2015-07-30 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: OpenCV 3.0 Computer Vision with Java is a practical tutorial guide that explains fundamental tasks from computer vision while focusing on Java development. This book will teach you how to set up OpenCV for Java and handle matrices using the basic operations of image processing such as filtering and image transforms. It will also help you learn how to use Haar cascades for tracking faces and to detect foreground and background regions with the help of a Kinect device. It will even give you insights into server-side OpenCV. Each chapter is presented with several projects that are ready to use. The functionality of these projects is found in many classes that allow developers to understand computer vision principles and rapidly extend or customize the projects for their needs.
Book Synopsis Machine Learning: End-to-End guide for Java developers by : Richard M. Reese
Download or read book Machine Learning: End-to-End guide for Java developers written by Richard M. Reese and published by Packt Publishing Ltd. This book was released on 2017-10-05 with total page 1159 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop, Implement and Tuneup your Machine Learning applications using the power of Java programming About This Book Detailed coverage on key machine learning topics with an emphasis on both theoretical and practical aspects Address predictive modeling problems using the most popular machine learning Java libraries A comprehensive course covering a wide spectrum of topics such as machine learning and natural language through practical use-cases Who This Book Is For This course is the right resource for anyone with some knowledge of Java programming who wants to get started with Data Science and Machine learning as quickly as possible. If you want to gain meaningful insights from big data and develop intelligent applications using Java, this course is also a must-have. What You Will Learn Understand key data analysis techniques centered around machine learning Implement Java APIs and various techniques such as classification, clustering, anomaly detection, and more Master key Java machine learning libraries, their functionality, and various kinds of problems that can be addressed using each of them Apply machine learning to real-world data for fraud detection, recommendation engines, text classification, and human activity recognition Experiment with semi-supervised learning and stream-based data mining, building high-performing and real-time predictive models Develop intelligent systems centered around various domains such as security, Internet of Things, social networking, and more In Detail Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning. The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection. The course includes premium content from three of our most popular books: Java for Data Science Machine Learning in Java Mastering Java Machine Learning On completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence. Style and approach This comprehensive course proceeds from being a tutorial to a practical guide, providing an introduction to machine learning and different machine learning techniques, exploring machine learning with Java libraries, and demonstrating real-world machine learning use cases using the Java platform.
Book Synopsis Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms by : Milutinovi?, Veljko
Download or read book Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms written by Milutinovi?, Veljko and published by IGI Global. This book was released on 2022-03-11 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.
Book Synopsis Algorithms for Image Processing and Computer Vision by : Jim R. Parker
Download or read book Algorithms for Image Processing and Computer Vision written by Jim R. Parker and published by John Wiley & Sons. This book was released on 1997 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: A cookbook of the hottest new algorithms and cutting-edge techniques in image processing and computer vision This amazing book/CD package puts the power of all the hottest new image processing techniques and algorithms in your hands. Based on J. R. Parker's exhaustive survey of Internet newsgroups worldwide, Algorithms for Image Processing and Computer Vision answers the most frequently asked questions with practical solutions. Parker uses dozens of real-life examples taken from fields such as robotics, space exploration, forensic analysis, cartography, and medical diagnostics, to clearly describe the latest techniques for morphing, advanced edge detection, wavelets, texture classification, image restoration, symbol recognition, and genetic algorithms, to name just a few. And, best of all, he implements each method covered in C and provides all the source code on the CD. For the first time, you're rescued from the hours of mind-numbing mathematical calculations it would ordinarily take to program these state-of-the-art image processing capabilities into software. At last, nonmathematicians get all the shortcuts they need for sophisticated image recognition and processing applications. On the CD-ROM you'll find: * Complete code for examples in the book * A gallery of images illustrating the results of advanced techniques * A free GNU compiler that lets you run source code on any platform * A system for restoring damaged or blurred images * A genetic algorithms package
Book Synopsis Practical Approach for Machine Learning and Deep Learning Algorithms by : Pandey Abhishek Kumar
Download or read book Practical Approach for Machine Learning and Deep Learning Algorithms written by Pandey Abhishek Kumar and published by BPB Publications. This book was released on 2019-09-20 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Guide covering topics from machine learning, regression models, neural network to tensor flow Key features Machine learning in MATLAB using basic concepts and algorithms. Deriving and accessing of data in MATLAB and next, pre-processing and preparation of data. Machine learning workflow for health monitoring. The neural network domain and implementation in MATLAB with explicit explanation of code and results. How predictive model can be improved using MATLAB? MATLAB code for an algorithm implementation, rather than for mathematical formula. Machine learning workflow for health monitoring. Description Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite processing. In this book, we have made an honest effort to make the concepts of machine learning easy and give basic programs in MATLAB right from the installation part. Although the real-time application of machine learning is endless, however, the basic concepts and algorithms are discussed using MATLAB language so that not only graduation students but also researchers are benefitted from it.What will you learn Pre-requisites to machine learning Finding natural patterns in data Building classification methods Data pre-processing in Python Building regression models Creating neural networks Deep learning Who this book is forThe book is basically meant for graduate and research students who find the algorithms of machine learning difficult to implement. We have touched all basic algorithms of machine learning in detail with a practical approach. Primarily, beginners will find this book more effective as the chapters are subdivided in a manner that they find the building and implementation of algorithms in MATLAB interesting and easy at the same time.Table of contents1. Pre-requisite to Machine Learning2. An introduction to Machine Learning3. Finding Natural Patterns in Data4. Building Classification Methods5. Data Pre-Processing in Python6. Building Regression Models7. Creating Neural Networks8. Introduction to Deep LearningAbout the authorAbhishek Kumar Pandey is pursuing his Doctorate in computer science and done M.Tech in Computer Sci. & Engineering. He has been working as an Assistant professor of Computer Science at Aryabhatt Engineering College and Research center, Ajmer and also visiting faculty in Government University MDS Ajmer. He has total Academic teaching experience of more than eight years with more than 50 publications in reputed National and International Journals. His research area includes- Artificial intelligence, Image processing, Computer Vision, Data Mining, Machine Learning. His Blog: http://veenapandey.simplesite.com/His LinkedIn Profile: https://www.linkedin.com/in/abhishek-pandey-ba6a6a64/ Pramod Singh Rathore is M. Tech in Computer Sci. and Engineering from Government Engineering College Ajmer, Rajasthan Technical University, Kota, India. He have been working as an Assistant Professor Computer Science at Aryabhatt Engineering College and Research center, Ajmer and also a visiting faculty in Government University Ajmer. He has authored a book in Network simulation which published worldwide. He has a total academic teaching experience more than 7 years with many publications in reputed national group, CRC USA, and has 40 publications as Research papers and Chapters in reputed National and International E-SCI SCOPUS. His research area includes machine learning, NS2, Computer Network, Mining, and DBMS. Dr S. Balamurugan is the Head of Research and Development, Quants IS & CS, India. Formely, he was the Director of Research and Development at Mindnotix Technologies, India. He has authored/co-authored 33 books and has 200 publications in various international journals and conferences to his credit. He was awarded with Three Post-Doctoral Degrees- Doctor of Science (D.Sc.) degree and Two Doctor of Letters(D.Litt) degrees for his significant contribution to research and development in Engineering, and is the recepient of thee Best Director Award, 2018. His biography is listed in "e;World Book of Researchers"e; 2018, Oxford, UK and in "e;Marquis WHO'S WHO"e; 2018 issue, New Jersey, USA. He carried out a healthcare consultancy project for VGM Hospitals between 2013 and 2016, and his current research projects include "e;Women Empowerment using IoT"e;, "e;Health-Aware Smart Chair"e;, "e;Advanced Brain Simulators for Assisting Physiological Medicine"e;, "e;Designing Novel Health Bands"e; and "e;IoT -based Devices for Assisting Elderly People"e;. His LinkedIn Profile: https://www.linkedin.com/in/dr-s-balamurugan-008a7512/
Book Synopsis Genetic Algorithms and Machine Learning for Programmers by : Frances Buontempo
Download or read book Genetic Algorithms and Machine Learning for Programmers written by Frances Buontempo and published by Pragmatic Bookshelf. This book was released on 2019-01-23 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
