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Download R: Unleash Machine Learning Techniques PDF

R: Unleash Machine Learning Techniques


Author :
Publisher : Packt Publishing Ltd
Release Date :
ISBN 10 : 9781787128286
Pages : 1123 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.2/5 (828 users download)

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Summary Book Review R: Unleash Machine Learning Techniques by Raghav Bali:

Download or read book R: Unleash Machine Learning Techniques written by Raghav Bali and published by Packt Publishing Ltd. This book was released on 2016-10-24 with total page 1123 pages. Available in PDF, EPUB and Kindle. Book excerpt: Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner. About This Book Build your confidence with R and find out how to solve a huge range of data-related problems Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today Don't just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis Who This Book Is For Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science What You Will Learn Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Solve interesting real-world problems using machine learning and R as the journey unfolds Write reusable code and build complete machine learning systems from the ground up Learn specialized machine learning techniques for text mining, social network data, big data, and more Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, big data, and more In Detail R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it... This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems. For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media. This Learning Path has been curated from three Packt products: R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar Machine Learning with R Learning - Second Edition By Brett Lantz Mastering Machine Learning with R By Cory Lesmeister Style and approach This is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.

Download Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems PDF

Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems


Author :
Publisher : CRC Press
Release Date :
ISBN 10 : 9781000441819
Pages : 296 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.4/5 (181 users download)

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Summary Book Review Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems by K. Suganthi:

Download or read book Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems written by K. Suganthi and published by CRC Press. This book was released on 2021-09-14 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems. The book offers an extensive overview on intelligent Wireless Communication systems and its underlying technologies, research challenges, solutions, and case studies. It provides information on intelligent wireless communication systems and its models, algorithms and applications. The book is written as a reference that offers the latest technologies and research results to various industry problems.

Download Computational Methods for Protein Structure Prediction and Modeling PDF

Computational Methods for Protein Structure Prediction and Modeling


Author :
Publisher : Springer Science & Business Media
Release Date :
ISBN 10 : 9780387683720
Pages : 396 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.8/5 (372 users download)

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Summary Book Review Computational Methods for Protein Structure Prediction and Modeling by Ying Xu:

Download or read book Computational Methods for Protein Structure Prediction and Modeling written by Ying Xu and published by Springer Science & Business Media. This book was released on 2007-08-24 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume One of this two-volume sequence focuses on the basic characterization of known protein structures, and structure prediction from protein sequence information. Eleven chapters survey of the field, covering key topics in modeling, force fields, classification, computational methods, and structure prediction. Each chapter is a self contained review covering definition of the problem and historical perspective; mathematical formulation; computational methods and algorithms; performance results; existing software; strengths, pitfalls, challenges, and future research.

Download Hands-On Data Science with R PDF

Hands-On Data Science with R


Author :
Publisher : Packt Publishing Ltd
Release Date :
ISBN 10 : 9781789135831
Pages : 420 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.3/5 (583 users download)

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Summary Book Review Hands-On Data Science with R by Vitor Bianchi Lanzetta:

Download or read book Hands-On Data Science with R written by Vitor Bianchi Lanzetta and published by Packt Publishing Ltd. This book was released on 2018-11-30 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide for professionals to perform various data science tasks in R Key FeaturesExplore the popular R packages for data scienceUse R for efficient data mining, text analytics and feature engineeringBecome a thorough data science professional with the help of hands-on examples and use-cases in RBook Description R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity. What you will learnUnderstand the R programming language and its ecosystem of packages for data scienceObtain and clean your data before processingMaster essential exploratory techniques for summarizing dataExamine various machine learning prediction, modelsExplore the H2O analytics platform in R for deep learningApply data mining techniques to available datasetsWork with interactive visualization packages in RIntegrate R with Spark and Hadoop for large-scale data analyticsWho this book is for If you are a budding data scientist keen to learn about the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course

Download Machine Learning in Chemistry PDF

Machine Learning in Chemistry


Author :
Publisher : Royal Society of Chemistry
Release Date :
ISBN 10 : 9781839160240
Pages : 546 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.6/5 (24 users download)

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Summary Book Review Machine Learning in Chemistry by Hugh M Cartwright:

Download or read book Machine Learning in Chemistry written by Hugh M Cartwright and published by Royal Society of Chemistry. This book was released on 2020-07-15 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.

Download Machine Learning for Ecology and Sustainable Natural Resource Management PDF

Machine Learning for Ecology and Sustainable Natural Resource Management


Author :
Publisher : Springer
Release Date :
ISBN 10 : 9783319969787
Pages : 441 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.6/5 (978 users download)

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Summary Book Review Machine Learning for Ecology and Sustainable Natural Resource Management by Grant Humphries:

Download or read book Machine Learning for Ecology and Sustainable Natural Resource Management written by Grant Humphries and published by Springer. This book was released on 2018-11-05 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

Download Multi-robot Exploration for Environmental Monitoring PDF

Multi-robot Exploration for Environmental Monitoring


Author :
Publisher : Academic Press
Release Date :
ISBN 10 : 9780128176085
Pages : 276 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.7/5 (68 users download)

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Summary Book Review Multi-robot Exploration for Environmental Monitoring by Kshitij Tiwari:

Download or read book Multi-robot Exploration for Environmental Monitoring written by Kshitij Tiwari and published by Academic Press. This book was released on 2019-11-29 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-robot Exploration for Environmental Monitoring: The Resource Constrained Perspective provides readers with the necessary robotics and mathematical tools required to realize the correct architecture. The architecture discussed in the book is not confined to environment monitoring, but can also be extended to search-and-rescue, border patrolling, crowd management and related applications. Several law enforcement agencies have already started to deploy UAVs, but instead of using teleoperated UAVs this book proposes methods to fully automate surveillance missions. Similarly, several government agencies like the US-EPA can benefit from this book by automating the process. Several challenges when deploying such models in real missions are addressed and solved, thus laying stepping stones towards realizing the architecture proposed. This book will be a great resource for graduate students in Computer Science, Computer Engineering, Robotics, Machine Learning and Mechatronics. Analyzes the constant conflict between machine learning models and robot resources Presents a novel range estimation framework tested on real robots (custom built and commercially available)

Download Machine Learning Made Easy with R PDF

Machine Learning Made Easy with R


Author :
Publisher :
Release Date :
ISBN 10 : 1546483756
Pages : 356 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.4/5 (648 users download)

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Summary Book Review Machine Learning Made Easy with R by N. Lewis:

Download or read book Machine Learning Made Easy with R written by N. Lewis and published by . This book was released on 2017-05-07 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finally, A Blueprint for Machine Learning with R! Machine Learning Made Easy with R offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle, fun and unhurried journey to creating machine learning models with R. Whether you are new to data science or a veteran, this book offers a powerful set of tools for quickly and easily gaining insight from your data using R. NO EXPERIENCE REQUIRED: This book uses plain language rather than a ton of equations; I'm assuming you never did like linear algebra, don't want to see things derived, dislike complicated computer code, and you're here because you want to try successful machine learning algorithms for yourself. YOUR PERSONAL BLUE PRINT: Through a simple to follow intuitive step by step process, you will learn how to use the most popular machine learning algorithms using R. Once you have mastered the process, it will be easy for you to translate your knowledge to assess your own data. THIS BOOK IS FOR YOU IF YOU WANT: Focus on explanations rather than mathematical derivation Practical illustrations that use real data. Illustrations to deepen your understanding. Worked examples in R you can easily follow and immediately implement. Ideas you can actually use and try on your own data. TAKE THE SHORTCUT: This guide was written for people just like you. Individuals who want to get up to speed as quickly as possible. to: YOU'LL LEARN HOW TO: Unleash the power of Decision Trees. Develop hands on skills using k-Nearest Neighbors. Design successful applications with Naive Bayes. Deploy Linear Discriminant Analysis. Explore Support Vector Machines. Master Linear and logistic regression. Create solutions with Random Forests. Solve complex problems with Boosting. Gain deep insights via K-Means clustering. Acquire tips to enhance model performance. For each machine learning algorithm, every step in the process is detailed, from preparing the data for analysis, to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks. Using plain language, this book offers a simple, intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using R. Everything you need to get started is contained within this book. Machine Learning Made Easy with R is your very own hands on practical, tactical, easy to follow guide to mastery. Buy this book today and accelerate your progress!

Download Signal Processing and Machine Learning for Biomedical Big Data PDF

Signal Processing and Machine Learning for Biomedical Big Data


Author :
Publisher : CRC Press
Release Date :
ISBN 10 : 9781498773461
Pages : 624 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.7/5 (346 users download)

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Summary Book Review Signal Processing and Machine Learning for Biomedical Big Data by Ervin Sejdic:

Download or read book Signal Processing and Machine Learning for Biomedical Big Data written by Ervin Sejdic and published by CRC Press. This book was released on 2018-07-04 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.

Download Convergence of Artificial Intelligence and the Internet of Things PDF

Convergence of Artificial Intelligence and the Internet of Things


Author :
Publisher : Springer Nature
Release Date :
ISBN 10 : 9783030449070
Pages : 439 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.4/5 (97 users download)

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Summary Book Review Convergence of Artificial Intelligence and the Internet of Things by George Mastorakis:

Download or read book Convergence of Artificial Intelligence and the Internet of Things written by George Mastorakis and published by Springer Nature. This book was released on 2020-05-06 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers recent research work on emerging Artificial Intelligence (AI) methods for processing and storing data generated by cloud-based Internet of Things (IoT) infrastructures. Major topics covered include the analysis and development of AI-powered mechanisms in future IoT applications and architectures. Further, the book addresses new technological developments, current research trends, and industry needs. Presenting case studies, experience and evaluation reports, and best practices in utilizing AI applications in IoT networks, it strikes a good balance between theoretical and practical issues. It also provides technical/scientific information on various aspects of AI technologies, ranging from basic concepts to research grade material, including future directions. The book is intended for researchers, practitioners, engineers and scientists involved in the design and development of protocols and AI applications for IoT-related devices. As the book covers a wide range of mobile applications and scenarios where IoT technologies can be applied, it also offers an essential introduction to the field.

Download R Data Analysis Cookbook PDF

R Data Analysis Cookbook


Author :
Publisher : Packt Publishing Ltd
Release Date :
ISBN 10 : 9781787125315
Pages : 560 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.2/5 (531 users download)

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Summary Book Review R Data Analysis Cookbook by Kuntal Ganguly:

Download or read book R Data Analysis Cookbook written by Kuntal Ganguly and published by Packt Publishing Ltd. This book was released on 2017-09-20 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 80 recipes to help you breeze through your data analysis projects using R About This Book Analyse your data using the popular R packages like ggplot2 with ready-to-use and customizable recipes Find meaningful insights from your data and generate dynamic reports A practical guide to help you put your data analysis skills in R to practical use Who This Book Is For This book is for data scientists, analysts and even enthusiasts who want to learn and implement the various data analysis techniques using R in a practical way. Those looking for quick, handy solutions to common tasks and challenges in data analysis will find this book to be very useful. Basic knowledge of statistics and R programming is assumed. What You Will Learn Acquire, format and visualize your data using R Using R to perform an Exploratory data analysis Introduction to machine learning algorithms such as classification and regression Get started with social network analysis Generate dynamic reporting with Shiny Get started with geospatial analysis Handling large data with R using Spark and MongoDB Build Recommendation system- Collaborative Filtering, Content based and Hybrid Learn real world dataset examples- Fraud Detection and Image Recognition In Detail Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data. This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way. By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios. Style and Approach Hands-on recipes to walk through data science challenges using R Your one-stop solution for common and not-so-common pain points while performing real-world problems to execute a series of tasks. Addressing your common and not-so-common pain points, this is a book that you must have on the shelf

Download Learning Social Media Analytics with R PDF

Learning Social Media Analytics with R


Author :
Publisher : Packt Publishing Ltd
Release Date :
ISBN 10 : 9781787125469
Pages : 394 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.2/5 (546 users download)

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Summary Book Review Learning Social Media Analytics with R by Raghav Bali:

Download or read book Learning Social Media Analytics with R written by Raghav Bali and published by Packt Publishing Ltd. This book was released on 2017-05-26 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms. Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering. Who This Book Is For It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise. What You Will Learn Learn how to tap into data from diverse social media platforms using the R ecosystem Use social media data to formulate and solve real-world problems Analyze user social networks and communities using concepts from graph theory and network analysis Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels Understand the art of representing actionable insights with effective visualizations Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In Detail The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights. Style and approach This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.

Download Recent Advances in Civil Engineering PDF

Recent Advances in Civil Engineering


Author :
Publisher : Springer Nature
Release Date :
ISBN 10 : 9789811918629
Pages : pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.1/5 (862 users download)

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Summary Book Review Recent Advances in Civil Engineering by Lakshman Nandagiri:

Download or read book Recent Advances in Civil Engineering written by Lakshman Nandagiri and published by Springer Nature. This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download Hands-On Artificial Intelligence for Android PDF

Hands-On Artificial Intelligence for Android


Author :
Publisher : BPB Publications
Release Date :
ISBN 10 : 9789355510242
Pages : 394 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.1/5 (24 users download)

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Summary Book Review Hands-On Artificial Intelligence for Android by Vasco Correia Veloso:

Download or read book Hands-On Artificial Intelligence for Android written by Vasco Correia Veloso and published by BPB Publications. This book was released on 2022-01-27 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build machine learning models and train them to make Android applications much smarter. KEY FEATURES ● Learn by doing, training, and evaluating your own machine learning models. ● Includes pre-trained TensorFlow models for image processing. ● Explains practical use cases of artificial intelligence in Android. DESCRIPTION This book features techniques and real implementations of machine learning applications on Android phones. This book covers various developer tools, including TensorFlow and Google ML Kit. The book begins with a quick review of android application development fundamentals and a couple of Java and Kotlin implementations developed using the Android Studio integrated development environment. The book explores TensorFlow Lite and Google ML Kit, along with some of the most widely used machine learning techniques. The book covers real projects on TensorFlow, demonstrates how to collect photos with Camera X, and preprocess them with the Google ML Kit. It explains how to onboard the power of machine learning in Android applications that detect images, identify faces, and apply effects to photographs, among other things. These applications are constructed on top of TensorFlow models – some of which were created and trained by the reader – and then converted to TensorFlow Lite for mobile applications. After reading the book, the reader will be able to apply machine learning techniques to create Android applications and take their applications to the next level. This book can be a successful tool to deep dive into Data Science for all mobile programmers. WHAT YOU WILL LEARN ● Get well-versed with Android Development and the fundamentals of AI. ● Learn to set up the ML environment with hands-on knowledge of TensorFlow. ● Build, train, and evaluate Machine Learning models. ● Practice ML by working on real face verification and identification applications. ● Explore cutting-edge models such as GAN and RNN in detail. ● Experience the use of CameraX, SQLite, and Google ML Kit on Android. WHO THIS BOOK IS FOR This book is intended for android developers, application engineers, machine learning engineers, and anybody interested in infusing intelligent, inventive, and smart features into mobile phones. Readers should have a basic understanding of the Java programming language. TABLE OF CONTENTS 1. Building an Application with Android Studio and Java 2. Event Handling and Intents in Android 3. Building our Base Application with Kotlin and SQLite 4. An Overview of Artificial Intelligence and Machine Learning 5. Introduction to TensorFlow 6. Training a Model for Image Recognition with TensorFlow 7. Android Camera Image Capture with CameraX 8. Using the Image Recognition Model in an Android Application 9. Detecting Faces with the Google ML Kit 10. Verifying Faces in Android with TensorFlow Lite 11. Registering Faces in the Application 12. Image Processing with Generative Adversarial Networks 13. Describing Images with NLP

Download R Data Analysis Cookbook, Second Edition PDF

R Data Analysis Cookbook, Second Edition


Author :
Publisher : Packt Publishing
Release Date :
ISBN 10 : 1787124479
Pages : 560 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.8/5 (712 users download)

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Summary Book Review R Data Analysis Cookbook, Second Edition by Kuntal Ganguly:

Download or read book R Data Analysis Cookbook, Second Edition written by Kuntal Ganguly and published by Packt Publishing. This book was released on 2017-09-20 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 80 recipes to help you breeze through your data analysis projects using RAbout This Book* Analyse your data using the popular R packages like ggplot2 with ready-to-use and customizable recipes* Find meaningful insights from your data and generate dynamic reports* A practical guide to help you put your data analysis skills in R to practical useWho This Book Is ForThis book is for data scientists, analysts and even enthusiasts who want to learn and implement the various data analysis techniques using R in a practical way. Those looking for quick, handy solutions to common tasks and challenges in data analysis will find this book to be very useful. Basic knowledge of statistics and R programming is assumed.What You Will Learn* Acquire, format and visualize your data using R* Using R to perform an Exploratory data analysis* Introduction to machine learning algorithms such as classification and regression* Get started with social network analysis* Generate dynamic reporting with Shiny* Get started with geospatial analysis* Handling large data with R using Spark and MongoDB* Build Recommendation system- Collaborative Filtering, Content based and Hybrid* Learn real world dataset examples- Fraud Detection and Image RecognitionIn DetailData analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data.This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way.By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios.Style and Approach* Hands-on recipes to walk through data science challenges using R* Your one-stop solution for common and not-so-common pain points while performing real-world problems to execute a series of tasks.* Addressing your common and not-so-common pain points, this is a book that you must have on the shelf

Download Composite Materials PDF

Composite Materials


Author :
Publisher : Elsevier
Release Date :
ISBN 10 : 9780128208793
Pages : 690 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.0/5 (879 users download)

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Summary Book Review Composite Materials by It Meng Low:

Download or read book Composite Materials written by It Meng Low and published by Elsevier. This book was released on 2021-06-18 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: Composite materials have been well developed to meet the challenges of high-performing material properties targeting engineering and structural applications. The ability of composite materials to absorb stresses and dissipate strain energy is vastly superior to that of other materials such as polymers and ceramics, and thus they offer engineers many mechanical, thermal, chemical and damage-tolerance advantages with limited drawbacks such as brittleness. Composite Materials: Manufacturing, Properties and Applications presents a comprehensive review of current status and future directions, latest technologies and innovative work, challenges and opportunities for composite materials. The chapters present latest advances and comprehensive coverage of material types, design, fabrication, modelling, properties and applications from conventional composite materials to advanced composites such as nanocomposites, self-healing and smart composites. The book targets researchers in the field of advanced composite materials and ceramics, students of materials science and engineering at the postgraduate level, as well as material engineers and scientists working in industrial R& D sectors for composite material manufacturing. Comprehensive coverage of material types, design, fabrication, modelling, properties and applications from conventional composite materials to advanced composites such as nanocomposites, self-healing and smart composites Features latest advances in terms of mechanical properties and other material parameters which are essential for designers and engineers in the composite and composite reinforcement manufacturing industry, as well as all those with an academic research interest in the subject Offers a good platform for end users to refer to the latest technologies and topics fitting into specific applications and specific methods to tackle manufacturing or material processing issues in relation to different types of composite materials

Download Neural Information Processing PDF

Neural Information Processing


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Publisher : Springer Science & Business Media
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ISBN 10 : 9783642106767
Pages : 898 pages
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Summary Book Review Neural Information Processing by Chi-Sing Leung:

Download or read book Neural Information Processing written by Chi-Sing Leung and published by Springer Science & Business Media. This book was released on 2009-11-24 with total page 898 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volumes LNCS 5863 and 5864 constitute the proceedings of the 16th International Conference on Neural Information Processing, ICONIP 2009, held in Bangkok, Thailand, in December 2009. The 145 regular session papers and 53 special session papers presented were carefully reviewed and selected from 466 submissions. The papers are structured in topical sections on cognitive science and computational neuroscience, neurodynamics, mathematical modeling and analysis, kernel and related methods, learning algorithms, pattern analysis, face analysis and processing, image processing, financial applications, computer vision, control and robotics, evolutionary computation, other emerging computational methods, signal, data and text processing, artificial spiking neural systems: nonlinear dynamics and engineering applications, towards brain-inspired systems, computational advances in bioinformatics, data mining for cybersecurity, evolutionary neural networks: theory and practice, hybrid and adaptive systems for computer vision and robot control, intelligent data mining, neural networks for data mining, and SOM and related subjects and its applications.