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R.N.A.S. Operations Reports

R.N.A.S. Operations Reports

Naval Military Press
2019
sidottu
The entire text of the R.N.A.S. OPERATIONS REPORTS from November 1915 To March 1918 are presented in this massive three volume set. These 53 OPERATIONS REPORTS were produced for internal consumption and provide a comprehensive coverage of observation and bombing sorties, damage inflicted and incurred in clashes with the enemy, intelligence gathered, aircrew are frequently mentioned by name.In addition to seaplanes, carrier-borne aircraft, and other aircraft with a legitimate "naval" application, the R.N.A.S. also maintained several crack fighter squadrons on the Western Front, as well as allocating scarce resources to an independent strategic bombing force at a time when such operations were highly speculative.During it's existence the R.N.A.S. had bases and stations in the UK, France, The Eastern Mediterranean, Durban, Otranto, Malta and Mombassa.The information in this huge three volume set is an absolute gold mine of facts, packed with revealing detail for Great War aviation historians, medal collectors and researchers.Flight Sub-Lieut. Fox, Killingholme Air Station, in a Sopwith Baby seaplane, proceeded in chase of a Zeppelin reported to be about 35 miles east of Spurn, He sighted the airship, and rising to 11,000 feet passed over it, dropping four 16-lb. bombs in succession and two boxes of Rauken darts.Hostile Aircraft.--Eighteen aeroplanes of the First Brigade taking photographs in the rear of the enemy's lines encountered 19 hostile machines. As the result of the fight three hostile machines were destroyed, three driven down damaged, and one driven down under control. The hostile formation was broken up, and the photographic machines completed their work.Lieutenant Musgrave and Corporal Jex, No. 45 Squadron, drove down a two-seater out of control, and Lieutenants Charlwood and Selby of the same squadron drove down another enemy machine out of control.Flight Sub-Lieut. Burt was obliged to land near the wrecked seaplane owing to his pressure failing. The Camel sank, and he swam about 400 yards from the wrecked enemy seaplane and was picked up by one of our destroyers.Pembroke.--Airship C. 3 carried out a patrol to Coningbeg between 0915 and 1715, during which an oil patch was sighted and bombed 5 miles south-west of St. Ann's Head at 1545. Oil rose to the surface after the first bomb was dropped; this was observed to increase after the second bomb had exploded.Flight Commander Price on wireless patrol attacked an Albatross Scout. Enemy aircraft went down in an almost vertical dive, and the Camel followed it for some way, but was not able to keep up with the enemy aircraft. This combat is confirmed by anti-aircraft battery.
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R.N.A.S. Operations Reports

R.N.A.S. Operations Reports

Naval Military Press
2019
sidottu
The entire text of the R.N.A.S. OPERATIONS REPORTS from November 1915 To March 1918 are presented in this massive three volume set. These 53 OPERATIONS REPORTS were produced for internal consumption and provide a comprehensive coverage of observation and bombing sorties, damage inflicted and incurred in clashes with the enemy, intelligence gathered, aircrew are frequently mentioned by name.In addition to seaplanes, carrier-borne aircraft, and other aircraft with a legitimate "naval" application, the R.N.A.S. also maintained several crack fighter squadrons on the Western Front, as well as allocating scarce resources to an independent strategic bombing force at a time when such operations were highly speculative.During it's existence the R.N.A.S. had bases and stations in the UK, France, The Eastern Mediterranean, Durban, Otranto, Malta and Mombassa.The information in this huge three volume set is an absolute gold mine of facts, packed with revealing detail for Great War aviation historians, medal collectors and researchers.Flight Sub-Lieut. Fox, Killingholme Air Station, in a Sopwith Baby seaplane, proceeded in chase of a Zeppelin reported to be about 35 miles east of Spurn, He sighted the airship, and rising to 11,000 feet passed over it, dropping four 16-lb. bombs in succession and two boxes of Rauken darts.Hostile Aircraft.--Eighteen aeroplanes of the First Brigade taking photographs in the rear of the enemy's lines encountered 19 hostile machines. As the result of the fight three hostile machines were destroyed, three driven down damaged, and one driven down under control. The hostile formation was broken up, and the photographic machines completed their work.Lieutenant Musgrave and Corporal Jex, No. 45 Squadron, drove down a two-seater out of control, and Lieutenants Charlwood and Selby of the same squadron drove down another enemy machine out of control.Flight Sub-Lieut. Burt was obliged to land near the wrecked seaplane owing to his pressure failing. The Camel sank, and he swam about 400 yards from the wrecked enemy seaplane and was picked up by one of our destroyers.Pembroke.--Airship C. 3 carried out a patrol to Coningbeg between 0915 and 1715, during which an oil patch was sighted and bombed 5 miles south-west of St. Ann's Head at 1545. Oil rose to the surface after the first bomb was dropped; this was observed to increase after the second bomb had exploded.Flight Commander Price on wireless patrol attacked an Albatross Scout. Enemy aircraft went down in an almost vertical dive, and the Camel followed it for some way, but was not able to keep up with the enemy aircraft. This combat is confirmed by anti-aircraft battery.
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R.N.A.S. Operations Reports

R.N.A.S. Operations Reports

NAVAL MILITARY PRESS
2019
sidottu
The entire text of the R.N.A.S. OPERATIONS REPORTS from November 1915 To March 1918 are presented in this massive three volume set. These 53 OPERATIONS REPORTS were produced for internal consumption and provide a comprehensive coverage of observation and bombing sorties, damage inflicted and incurred in clashes with the enemy, intelligence gathered, aircrew are frequently mentioned by name.In addition to seaplanes, carrier-borne aircraft, and other aircraft with a legitimate "naval" application, the R.N.A.S. also maintained several crack fighter squadrons on the Western Front, as well as allocating scarce resources to an independent strategic bombing force at a time when such operations were highly speculative.During it's existence the R.N.A.S. had bases and stations in the UK, France, The Eastern Mediterranean, Durban, Otranto, Malta and Mombassa.The information in this huge three volume set is an absolute gold mine of facts, packed with revealing detail for Great War aviation historians, medal collectors and researchers.Flight Sub-Lieut. Fox, Killingholme Air Station, in a Sopwith Baby seaplane, proceeded in chase of a Zeppelin reported to be about 35 miles east of Spurn, He sighted the airship, and rising to 11,000 feet passed over it, dropping four 16-lb. bombs in succession and two boxes of Rauken darts.Hostile Aircraft.--Eighteen aeroplanes of the First Brigade taking photographs in the rear of the enemy's lines encountered 19 hostile machines. As the result of the fight three hostile machines were destroyed, three driven down damaged, and one driven down under control. The hostile formation was broken up, and the photographic machines completed their work.Lieutenant Musgrave and Corporal Jex, No. 45 Squadron, drove down a two-seater out of control, and Lieutenants Charlwood and Selby of the same squadron drove down another enemy machine out of control.Flight Sub-Lieut. Burt was obliged to land near the wrecked seaplane owing to his pressure failing. The Camel sank, and he swam about 400 yards from the wrecked enemy seaplane and was picked up by one of our destroyers.Pembroke.--Airship C. 3 carried out a patrol to Coningbeg between 0915 and 1715, during which an oil patch was sighted and bombed 5 miles south-west of St. Ann's Head at 1545. Oil rose to the surface after the first bomb was dropped; this was observed to increase after the second bomb had exploded.Flight Commander Price on wireless patrol attacked an Albatross Scout. Enemy aircraft went down in an almost vertical dive, and the Camel followed it for some way, but was not able to keep up with the enemy aircraft. This combat is confirmed by anti-aircraft battery.
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R Object-oriented Programming

R Object-oriented Programming

Kelly Black

Packt Publishing Limited
2014
nidottu
This book is designed for people with some experience in basic programming practices. It is also assumed that they have some basic experience using R and are familiar using the command line in an R environment. Our primary goal is to raise a beginner to a more advanced level to make him/her more comfortable creating programs and extending R to solve common problems.
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R Machine Learning Essentials

R Machine Learning Essentials

Michele Usuelli

Packt Publishing Limited
2014
nidottu
If you want to learn how to develop effective machine learning solutions to your business problems in R, this book is for you. It would be helpful to have a bit of familiarity with basic object-oriented programming concepts, but no prior experience is required.
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R Graphs Cookbook

R Graphs Cookbook

Jaynal Abedin; Hrishi V. Mittal

Packt Publishing Limited
2014
nidottu
Targeted at those with an existing familiarity with R programming, this practical guide will appeal directly to programmers interested in learning effective data visualization techniques with R and a wide-range of its associated libraries.
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R for Data Science Cookbook

R for Data Science Cookbook

Yu-Wei Chiu)

Packt Publishing Limited
2016
nidottu
Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques About This Book • Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages • Understand how to apply useful data analysis techniques in R for real-world applications • An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis Who This Book Is For This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages. What You Will Learn • Get to know the functional characteristics of R language • Extract, transform, and load data from heterogeneous sources • Understand how easily R can confront probability and statistics problems • Get simple R instructions to quickly organize and manipulate large datasets • Create professional data visualizations and interactive reports • Predict user purchase behavior by adopting a classification approach • Implement data mining techniques to discover items that are frequently purchased together • Group similar text documents by using various clustering methods In Detail This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis. Style and approach This easy-to-follow guide is full of hands-on examples of data analysis with R. Each topic is fully explained beginning with the core concept, followed by step-by-step practical examples, and concluding with detailed explanations of each concept used.
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R Machine Learning By Example

R Machine Learning By Example

Raghav Bali; Dipanjan Sarkar

Packt Publishing Limited
2016
nidottu
Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully About This Book • Get to grips with the concepts of machine learning through exciting real-world examples • Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning • Learn to build your own machine learning system with this example-based practical guide Who This Book Is For If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary. What You Will Learn • Utilize the power of R to handle data extraction, manipulation, and exploration techniques • Use R to visualize data spread across multiple dimensions and extract useful features • Explore the underlying mathematical and logical concepts that drive machine learning algorithms • Dive deep into the world of analytics to predict situations correctly • Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action • Write reusable code and build complete machine learning systems from the ground up • Solve interesting real-world problems using machine learning and R as the journey unfolds • Harness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data science In Detail Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems. You'll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms. Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R. Style and approach The book is an enticing journey 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 real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.
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R for Data Science

R for Data Science

Dan Toomey

Packt Publishing Limited
2014
nidottu
If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.
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R Data Science Essentials

R Data Science Essentials

Raja B. Koushik; Sharan Kumar Ravindran

Packt Publishing Limited
2016
nidottu
Learn the essence of data science and visualization using R in no time at all About This Book • Become a pro at making stunning visualizations and dashboards quickly and without hassle • For better decision making in business, apply the R programming language with the help of useful statistical techniques. • From seasoned authors comes a book that offers you a plethora of fast-paced techniques to detect and analyze data patterns Who This Book Is For If you are an aspiring data scientist or analyst who has a basic understanding of data science and has basic hands-on experience in R or any other analytics tool, then R Data Science Essentials is the book for you. What You Will Learn • Perform data preprocessing and basic operations on data • Implement visual and non-visual implementation data exploration techniques • Mine patterns from data using affinity and sequential analysis • Use different clustering algorithms and visualize them • Implement logistic and linear regression and find out how to evaluate and improve the performance of an algorithm • Extract patterns through visualization and build a forecasting algorithm • Build a recommendation engine using different collaborative filtering algorithms • Make a stunning visualization and dashboard using ggplot and R shiny In Detail With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision. Style and approach This easy-to-follow guide contains hands-on examples of the concepts of data science using R.
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R.E.M.

R.E.M.

Tony Fletcher

Omnibus Press
2018
nidottu
Formed in Athens, Georgia in 1980, R.E.M. released their first single, Radio Free Europe, in 1981. By the time the band broke up they had recorded fifteen studio albums, 63 singles, sold more than 85 million records, signed the biggest recording contract in music history, and were inducted into the Rock and Roll Hall of Fame in their first year of eligibility.
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R Data Structures and Algorithms

R Data Structures and Algorithms

Dr. PKS Prakash; Achyutuni Sri Krishna Rao

Packt Publishing Limited
2016
nidottu
Increase speed and performance of your applications with efficient data structures and algorithms About This Book • See how to use data structures such as arrays, stacks, trees, lists, and graphs through real-world examples • Find out about important and advanced data structures such as searching and sorting algorithms • Understand important concepts such as big-o notation, dynamic programming, and functional data structured Who This Book Is For This book is for R developers who want to use data structures efficiently. Basic knowledge of R is expected. What You Will Learn • Understand the rationality behind data structures and algorithms • Understand computation evaluation of a program featuring asymptotic and empirical algorithm analysis • Get to know the fundamentals of arrays and linked-based data structures • Analyze types of sorting algorithms • Search algorithms along with hashing • Understand linear and tree-based indexing • Be able to implement a graph including topological sort, shortest path problem, and Prim's algorithm • Understand dynamic programming (Knapsack) and randomized algorithms In Detail In this book, we cover not only classical data structures, but also functional data structures. We begin by answering the fundamental question: why data structures? We then move on to cover the relationship between data structures and algorithms, followed by an analysis and evaluation of algorithms. We introduce the fundamentals of data structures, such as lists, stacks, queues, and dictionaries, using real-world examples. We also cover topics such as indexing, sorting, and searching in depth. Later on, you will be exposed to advanced topics such as graph data structures, dynamic programming, and randomized algorithms. You will come to appreciate the intricacies of high performance and scalable programming using R. We also cover special R data structures such as vectors, data frames, and atomic vectors. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. We will also explore the application of binary search and will go in depth into sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. Style and approach This easy-to-read book with its fast-paced nature will improve the productivity of an R programmer and improve the performance of R applications. It is packed with real-world examples.
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R. S. Thomas to Rowan Williams

R. S. Thomas to Rowan Williams

M. Wynn Thomas

UNIVERSITY OF WALES PRESS
2022
nidottu
The great religious poetry of R. S. Thomas and the poetry of the former Archbishop of Canterbury Rowan Williams is rooted in a remarkable late-twentieth-century tradition of spiritual poetry in Wales that includes figures as different as Saunders Lewis and Vernon Watkins, Waldo Williams and Bobi Jones. Examining this body of work in detail, the present study demonstrates how the different theological outlooks of the poets was reflected in their choice of form, style and vocabulary, highlighting a literary culture that was highly unusual in its rejection of a prevailing secularisation in the UK, Western Europe and the USA.
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R Deep Learning Cookbook

R Deep Learning Cookbook

Dr. PKS Prakash; Achyutuni Sri Krishna Rao

Packt Publishing Limited
2017
nidottu
Powerful, independent recipes to build deep learning models in different application areas using R libraries About This Book • Master intricacies of R deep learning packages such as mxnet & tensorflow • Learn application on deep learning in different domains using practical examples from text, image and speech • Guide to set-up deep learning models using CPU and GPU Who This Book Is For Data science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful. What You Will Learn • Build deep learning models in different application areas using TensorFlow, H2O, and MXnet. • Analyzing a Deep boltzmann machine • Setting up and Analysing Deep belief networks • Building supervised model using various machine learning algorithms • Set up variants of basic convolution function • Represent data using Autoencoders. • Explore generative models available in Deep Learning. • Discover sequence modeling using Recurrent nets • Learn fundamentals of Reinforcement Leaning • Learn the steps involved in applying Deep Learning in text mining • Explore application of deep learning in signal processing • Utilize Transfer learning for utilizing pre-trained model • Train a deep learning model on a GPU In Detail Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems. Style and approach Collection of hands-on recipes that would act as your all-time reference for your deep learning needs
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R Data Mining

R Data Mining

Andrea Cirillo

Packt Publishing Limited
2017
nidottu
Mine valuable insights from your data using popular tools and techniques in R About This Book • Understand the basics of data mining and why R is a perfect tool for it. • Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it. • Apply effective data mining models to perform regression and classification tasks. Who This Book Is For If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required. What You Will Learn • Master relevant packages such as dplyr, ggplot2 and so on for data mining • Learn how to effectively organize a data mining project through the CRISP-DM methodology • Implement data cleaning and validation tasks to get your data ready for data mining activities • Execute Exploratory Data Analysis both the numerical and the graphical way • Develop simple and multiple regression models along with logistic regression • Apply basic ensemble learning techniques to join together results from different data mining models • Perform text mining analysis from unstructured pdf files and textual data • Produce reports to effectively communicate objectives, methods, and insights of your analyses In Detail R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets. Style and approach This book takes a practical, step-by-step approach to explain the concepts of data mining. Practical use-cases involving real-world datasets are used throughout the book to clearly explain theoretical concepts.
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R Data Analysis Cookbook -

R Data Analysis Cookbook -

Kuntal Ganguly

Packt Publishing Limited
2017
nidottu
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
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