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"R is an open source programming language and interactive programming environment that has become the software tool of choice in data analytics. Learning Base R provides an introduction to the language for those with and without prior programming experience. It introduces the key topics that you will need to begin analyzing data and programming in R. The focus here is on the R language rather than a particular application. Within the text, there are 200 exercises to assess your R skills."--Back cover.
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Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for computer vision and text Apply deep learning techniques in cloud for large-scale processing Build, train, and optimize neural network models on a range of datasets Book Description Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You'll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects. What you will learn Build shallow neural network prediction models Prevent models from overfitting the data to improve generalizability Explore techniques for finding the best hyperparameters for deep learning models Create NLP models using Keras and TensorFlow in R Use deep learning for computer vision tasks Implement deep learning tasks, such as NLP, recommendation systems, and autoencoders Who this book is for This second edition of R Deep Learning Essentials is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. Fundamental understanding of the R language is necessary to get the most out of this book.
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Practical examples with real-world projects in GIS, Remote sensing, Geospatial data management and Analysis using the R programming language Key Features Understand the basics of R and QGIS to work with GIS and remote sensing data Learn to manage, manipulate, and analyze spatial data using R and QGIS Apply machine learning algorithms to geospatial data using R and QGIS Book Description Managing spatial data has always been challenging and it's getting more complex as the size of data increases. Spatial data is actually big data and you need different tools and techniques to work your way around to model and create different workflows. R and QGIS have powerful features that can make this job easier. This book is your companion for applying machine learning algorithms on GIS and remote sensing data. You'll start by gaining an understanding of the nature of spatial data and installing R and QGIS. Then, you'll learn how to use different R packages to import, export, and visualize data, before doing the same in QGIS. Screenshots are included to ease your understanding. Moving on, you'll learn about different aspects of managing and analyzing spatial data, before diving into advanced topics. You'll create powerful data visualizations using ggplot2, ggmap, raster, and other packages of R. You'll learn how to use QGIS 3.2.2 to visualize and manage (create, edit, and format) spatial data. Different types of spatial analysis are also covered using R. Finally, you'll work with landslide data from Bangladesh to create a landslide susceptibility map using different machine learning algorithms. By reading this book, you'll transition from being a beginner to an intermediate user of GIS and remote sensing data in no time. What you will learn Install R and QGIS Get familiar with the basics of R programming and QGIS Visualize quantitative and qualitative data to create maps Find out the basics of raster data and how to use them in R and QGIS Perform geoprocessing tasks and automate them using the graphical modeler of QGIS Apply different machine learning algorithms on satellite data for landslide susceptibility mapping and prediction Who this book is for This book is great for geographers, environmental scientists, statisticians, and every professional who deals with spatial data. If you want to learn how to handle GIS and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful but is not necessary.
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Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts About This Book Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily Who This Book Is For Although budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status , will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure. What You Will Learn Master the steps involved in the predictive modeling process Grow your expertise in using R and its diverse range of packages Learn how to classify predictive models and distinguish which models are suitable for a particular problem Understand steps for tidying data and improving the performing metrics Recognize the assumptions, strengths, and weaknesses of a predictive model Understand how and why each predictive model works in R Select appropriate metrics to assess the performance of different types of predictive model Explore word embedding and recurrent neural networks in R Train models in R that can work on very large datasets In Detail R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do y...
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Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key Features Implement deep learning algorithms to build AI models with the help of tips and tricks Understand how deep learning models operate using expert techniques Apply reinforcement learning, computer vision, GANs, and NLP using a range of datasets Book Description Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples. What you will learn Learn how to create binary and multi-class deep neural network models Implement GANs for generating new images Create autoencoder neural networks for image dimension reduction, image de-noising and image correction Implement deep neural networks for performing efficient text classification Learn to define a recurrent convolutional network model for classification in Keras Explore best practices and tips for performance optimization of various deep learning models Who this book is for This book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the...
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Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more Key Features Master machine learning, deep learning, and predictive modeling concepts in R 3.5 Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains Implement smart cognitive models with helpful tips and best practices Book Description R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you'll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You'll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations. What you will learn Explore deep neural networks and various frameworks that can be used in R Develop a joke recommendation engine to recommend jokes that match users' tastes Create powerful ML models with ensembles to predict employee attrition Build autoencoders for credit card fraud detection Work with image recognition and convolutional neural networks Make predictions for casino slot machine using reinforcement learning Implement NLP techniques for sentiment analysis and customer segmentation Who this book is for If you're a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this book.
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This new book written by the developers of R Markdown is an essential reference that will help users learn and make full use of the software. Those new to R Markdown will appreciate the short, practical examples that address the most common issues users encounter. Frequent users will also benefit from the wide ranging tips and tricks that expose hidden' features, support customization and demonstrate the many new and varied applications of the software. After reading this book users will learn how to: Enhance your R Markdown content with diagrams, citations, and dynamically generated text Streamline your workflow with child documents, code chunk references, and caching Control the formatting and layout with Pandoc markdown syntax or by writing custom HTML and LaTeX templates Utilize chunk options and hooks to fine-tune how your code is processed Switch between different language engineers to seamlessly incorporate python, D3, and more into your analysis
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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 ...
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