An empowering and inspirational guide to uplift the mood.
An inspirational guide to uplift the mood.
Je commençais à régler cette affaire de divorce sans perdre trop de plumes, quand j’appris la mort de mamie Rose. Elle s’était éteinte dans son sommeil. Je savais qu’un jour, cela arriverait mais je n’y étais pas préparée. Ma vie s’écroula de nouveau. Je n’avais plus rien ici. Sauf Georgia, ma nounou amérindienne qui m’a tant appris. Je lui ai fait faire des magnifiques funérailles. Elle est enterrée dans le parc, près de papa et de maman. J’ai pleuré pendant trois jours et trois nuits. Brett qui disait m’aimer à la folie, refusant toujours le divorce n’est même pas venu : trop occupé à baiser d’autres filles. Je parle longuement à Georgia, elle me conseille de repartir là où est ma famille car pour son peuple la famille est très importante et ma famille est en France. Je suis arrivé, il y a un mois. J'ai fait table rase du passé. Je suis en pleine procédure de divorce. Il va me couter un bras. Quelle salope. Pourtant je l’aimais sincèrement. Il n'y avait qu’elle. J'ai appris qu'elle avait un amant. J'ai voulu me venger et quel couillon, je me suis fait prendre. Elle m'a fait suivre par un détective et il a pris des photos. J'ai donc tous les torts. Mais je ne veux pas lui donner raison. Je me bagarre. J'ai un bon avocat. Elle partira avec le minimum. Même si ça doit prendre deux ans. Je suis donc heureux de revenir en France je revoie ma famille et mes amis. Adieu Londres !
Key FeaturesAnalyse your data using the popular R packages with ready-to-use and customizable recipesFind meaningful insights from your data and generate dynamic reportsA practical guide to help you put your data analysis skills in R to practical useBook DescriptionThis 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.What you will learnAcquire, format and visualize your data using RUsing R to perform an Exploratory data analysisIntroduction to machine learning algorithms such as classification and regressionGet started with social network analysisGenerate dynamic reporting with ShinyGet started with geospatial analysisHandling large data with R for Spark and MongoDBAbout the AuthorKuntal Ganguly is a Big Data Analytics engineer at Amazon, focused on building large scale data driven analytics system using Big Data frameworks and Machine Learning. He has around 7years of experience building several Big data and Machine Learning applications.Kuntal provides solutions to AWS customers in building real-time analytics system using AWS services and open source Hadoop ecosystem technologies like Spark, Kafka, Storm, Flink along with Machine Learning and Deep Learning framework.Kuntal enjoys hands-on software development, and has single-handedly conceived, architected, developed, and deployed several large scale distributed applications. Besides being an open source contributor, he is a Machine Learning, Deep Learning practitioner and very passionate about building Intelligent Applications.
Key FeaturesYour handy guide to take your understanding of data analysis with R to the next level.Real-world projects focusing on problems in finance, network analysis, social media and more.From data manipulation to analysis to visualization in R, this book will teach you everything you need to know about building end to end data analysis pipelines using R.Book DescriptionR offers a large variety of packages and libraries for fast and accurate data analysis and visualisation. As a result, it is one of the most popularly used languages by Data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle.You will start with building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You will implement time series modeling for anomaly detection, and understand cluster analysis of streaming data. Projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately are also provided with easy to follow codes. With the help of these real-world projects, you will get a better understanding of the challenges faced in building data analysis pipelines, and how you can overcome them without compromising on the efficiency or accuracy of your systems. The book covers some popularly used R packages such as dplyr, ggplot2, RShiny and others, and includes tips on using them effectively.By the end of this book, you will have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle.What you will learnBuild end to end predictive analytics systems in RBuild an experimental design to gather your own data and conduct analysisBuild a recommender system from scratch using different approaches.Use and leverage RShiny to build reactive programming applicationsBuild systems for varied domains including Market Research, Network Analysis, Social media analysis, and moreExplore various R Packages like, RShiny, ggplot, recommenderlab, dplyr, and learn how to use them effectivelyCommunicate modeling results using Shiny DashboardsPerform multi-variate time series analysis prediction, supplemented with sensitivity analysis and risk modelingAbout the AuthorGopi Subramanian is a data scientist with over 15 years of experience in the field of data mining and machine learning. During the past decade, he has designed, conceived, developed, and led data mining, text mining, natural language processing, information extraction and retrieval, and search systems for various domains and business verticals, including engineering infrastructure, consumer finance, healthcare, and materials. In the loyalty domain, he has conceived and built innovative consumer loyalty models and designed enterprise-wide systems for personalized promotions. He has filed over ten patent applications at the US and Indian patent office and has several publications to his credit. He currently lives and works in Bangaluru, India
Key FeaturesUse R’s popular packages such as ggplot2, ggvis, ggforce, and more, to create custom, interactive visualization solutions.Create, design and build interactive dashboards using ShinyA highly practical guide to help you get to grips with the basics of data visualization techniques, and how you can implement them using RBook DescriptionR is an open source language for data analysis and graphics. It is platform-independent and allows users to load various packages as well as develop their own packages to interpret data better.It’s popularity has sky-rocketed in the recent years because of its powerful capabilities when it comes to turning different kinds of data into intuitive visualization solutions.This book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization with R. It starts off with the basics of R plots and an introduction to creating maps and customizing them, before gradually taking you through creating interactive maps using the ggvis package, generating choropleth maps, projection types and creating a journal publishable high quality map. You will then learn how to design a three-dimensional plot in plotly.By the end of the book, you will be equipped with the key techniques to create impressive data visualizations with professional efficiency and precision.What you will learnGet to know various data visualization libraries available in R to represent dataGenerate various plots in R using the basic R plotting techniquesAdd elements, text, animation, and colors to your plot to make sense of dataDeepen your knowledge by adding bar-charts, scatterplots, and time series plots using ggplot2Build interactive dashboards using Shiny.Coloring particular regions of the map based on the values of a variable in your data frameCreate high quality journal publishable scatterplotsAbout the AuthorVitor Bianchi Lanzetta has really been into R. Using R both for thesis and during his leisure.Vitor fitted several neural networks models for commodities price predicting. As a graduation student he was called to join the university's team into the CFA Challenge, in wich among other things he programmed a monte carlo simuation from his team's model by using R.
Key FeaturesMaster intricacies of R deep learning packages such as mxnet & tensorflowLearn application on deep learning in different domains using practical examples from text, image and speechGuide to set-up deep learning models using CPU and GPUBook DescriptionDeep Learning is the next big thing. It is a part of machine learning. Its favorable results in application with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. With the growth in Deep Learning, the inter relation between R and deep learning is growing tremendously as they are very compatible with each other in attaining the various results.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 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.What you will learnBuild deep learning models in different application areas using H20, MXnet.Analyzing a Deep boltzmann machineSetting up and Analysing Deep belief networksGenerating a RNN-RBM hybrid model for sequence generationBuilding supervised model using various machine learning algorithmsSet up variants of basic convolution functionRepresent data using Autoencoders.Explore generative models available in Deep Learning.Implement Branching Program Machines for structured or sequential outputsDiscover sequence modeling using Recurrent and Recursive netsLearn the steps involved in applying Deep Learning in text miningTrain a deep learning model on a GPU
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.You’ll learn how to:Wrangle—transform your datasets into a form convenient for analysisProgram—learn powerful R tools for solving data problems with greater clarity and easeExplore—examine your data, generate hypotheses, and quickly test themModel—provide a low-dimensional summary that captures true "signals" in your datasetCommunicate—learn R Markdown for integrating prose, code, and results
This book discusses advanced topics such as R core programing, object oriented R programing, parallel computing with R, and spatial data types. The author leads readers to merge mature and effective methdologies in traditional programing to R programing. It shows how to interface R with C, Java, and other popular programing laguages and platforms.
Inspecting divisibility, performing calculations and prime numbers are classic topics of number theory. Inspecting divisibility is the process of determining if a number is divisible by another without actually performing the division. An example of that is divisibility by 3 which can be done by adding the digits of a given number. If the sum is divisible by 3 then so is the number in question, if not then it isn’t. For instance 23151 is divisible by 3 because 2 + 3 + 1 + 5 + 1 = 12 is divisible by 3. 1425 isn’t because 1 + 4 + 3 +5 = 13 is not divisible by 3. Another example is divisibility by 11. A number is divisible by 11 if the alternating sum of its digits is divisible by 11. 1254 is divisible by 11 because 1 – 2 + 5 – 4 = 0. Then there is divisibility by 2 and 5. A number is divisible by 2 if it ends with an even digit and is divisible by 5 if ends with 0 or 5. These methods, while simple, are limited to 2, 3, 5, 9, 11 and some of their multiples and there are no known methods for other numbers such as 7, 13, 23 etc…At first the text discusses some indirect methods which could be used to serve that purpose using remainderial coefficients. Yet these indirect methods are also arduous and do not have the simplicity and intuition of the methods discussed above with 2, 3, 5, 9 and 11.So the text introduces a simpler method of inspecting divisibility for all numbers, done by using a function called the R function. It then redefines how division and multiplication are performed using the function which would prove faster and easier.Primes are the other topic of this book. Inspecting the primality of numbers and finding primes are long standing issues in mathematics and various methods to do that exist. But the book uses the R function introduced earlier for inspecting divisibility to accomplish these tasks with a uniform method. Inspecting primality is the process of determining whether a number is prime or not and finding primes is simply the process of finding the next prime from a known one. In this book, both are done by enhanced elimination combined with the theorems and axioms learned about the R function in the prior chapters.
Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they don't teach you which charts to use according to the type of the data. In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems. Next, we provide more than 200 practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics.With this book, you 'll learn: - How to quickly create beautiful graphics using ggplot2 packages- How to properly customize and annotate the plots- Type of graphics for visualizing categorical and continuous variables- How to add automatically p-values to box plots, bar plots and alternatives- How to add marginal density plots and correlation coefficients to scatter plots- Key methods for analyzing and visualizing multivariate data - R functions and packages for plotting time series data - How to combine multiple plots on one page to create production-quality figures.
Questo libro è parte integrante di me, ho cercato di scrivere ciò che ho dentro visto che pochi mi conoscono davvero, in 50 frasi/poesie/riflessioni ho provato a trasmettere emozioni, e spero di esserci riuscito . Spesso condividiamo pezzi di cuore con le persone sbagliate .
Maddie and Daniel have been up to quite a bit of fun. Now it might be time for them to get serious and start talking about the future.This is episode 18 in The ABCs of Swinging. Look for all 26 episodes in 1 download coming out February 14, 2018. Until then, if you have #KindleUnlimited, download them one at a time for free.Sign up for Bella Bordeaux's newsletter today and learn of new releases, contests, & more: http://eepurl.com/bL_3ZH
The flight attendant announced, "ladies and gentlemen, welcome to Las Vegas.Bradley Fox turned to Elizabeth Daniels and said, "we're here. Remember, the saying is, whatever happens in Vegas, stays in Vegas.""Well then," Beth replied, "let's go make something happen."And they did; from Fort Lauderdale to Las Vegas, to New York, to the Caribbean. With the struggles of a new relationship behind them, they were now free to enjoy the love they felt for each other.Their businesses were growing and their romance was happening.
Hypocrite or hero? You decide after reading The Confession. A short story which questions what goodness and honesty really mean.
Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. This new edition adds coverage of R Studio and reproducible research.
Make the most of R’s extensive toolset R Projects For Dummies offers a unique learn-by-doing approach. You will increase the depth and breadth of your R skillset by completing a wide variety of projects. By using R’s graphics, interactive, and machine learning tools, you’ll learn to apply R’s extensive capabilities in an array of scenarios. The depth of the project experience is unmatched by any other content online or in print. And you just might increase your statistics knowledge along the way, too! R is a free tool, and it’s the basis of a huge amount of work in data science. It's taking the place of costly statistical software that sometimes takes a long time to learn. One reason is that you can use just a few R commands to create sophisticated analyses. Another is that easy-to-learn R graphics enable you make the results of those analyses available to a wide audience. This book will help you sharpen your skills by applying them in the context of projects with R, including dashboards, image processing, data reduction, mapping, and more. Appropriate for R users at all levels Helps R programmers plan and complete their own projects Focuses on R functions and packages Shows how to carry out complex analyses by just entering a few commands If you’re brand new to R or just want to brush up on your skills, R Projects For Dummies will help you complete your projects with ease.
Los hijos del error son muchos, desde el accidente hasta la enfermedad pasando por la derrota deportiva y la ruina personal (por quedarse sin un céntimo en el casino o asociarse con la persona menos indicada). ¿Y por qué existe el error? Es una pregunta nada trivial cuya respuesta no es evidente. Así como el estudio de la enfermedad puede ayudar a entender mejor la salud, el del error puede arrojar luz sobre el fenómeno del conocimiento y acercarnos a la verdad.Este es un libro sobre las causas, consecuencias, manifestaciones y profundas implicaciones de un concepto que toca palos tan diversos y fundamentales como la vida, la enfermedad, la muerte, el azar, el libre albedrío, la maldad, la evolución, la felicidad, la ignorancia, la estupidez... Lo cierto es que pocas cosas de las realmente importantes quedan fuera de su alcance.
Key FeaturesLearn some of the widely used R packages such as Snowball, Tm, plyr, ggplot2 to perform various complexities in the text mining process.Hands-on recipes to implement real-world case studies and practical examples to illustrate text mining techniques.Develop your skills by an in-depth understanding of text mining process using R with this easy-to-follow guide.Book DescriptionThe most natural form of storing information is text. Text mining, also known as text data mining or text analytics, refers to the process of extracting interesting and hidden patterns from text documents. R provides an extensive ecosystem with the help of their widely used packages like Snowball, Tm, plyr, ggplot2 and many more. R Text Mining Cookbook will take you through all the required text mining concepts, statistics, tools, how to set up them, algorithms and their implementations in real world. You will be made familiar to work with text data of different complexities. This book will address text mining pain points such as -- working with unstructured or fuzzy data,solving semantic issues, entity recognition with the help of independent and insightful recipes. By the end of this book you will be able to mine text data by implementing set of tasks covered in our easy-to-follow recipes.What you will learnDiscovering Syntagmatic RelationAnalysing Word Association MiningImplementing of text mining package in RQuantitatifying discourse analysis of transcriptsApplying textmining using Rattle GUIHandling white spacesImplementation of text mining using Big dataAbout the AuthorGururaghav Gopal is presently working in Paterson securities as Quant developer/trader consultant.Previously he worked as Data science consultant and was associated with an e-commerce industry. He has also been teaching graduate and post graduate student of VIT Vellore in the area of pattern recognition.He is been associated with several research industries as research associates namely IFMR, NAL.He did his bachelor in electrical and electronics eng with masters in computer science and engineering.He did his course work from IFMR in financial engineering and risk management and from then has been associated with the financial industry. He has won few awards and has few international publications to his credit.He is interested in programming, teaching and doing consulting work. During his free time, he listens to music.
At a more professional level, choice of a leader to run his organisation in a particular manner, choice of hiring some special kind of people to shape the company's future.A leader's decision to seek guidance from an executive coach rather than doing it all by himself has an equally significant impact on the fortunes of many people working in the Organisation.They consider Executive coaching as a one-time interactive session of a few hours where the coach would arrive and instil all his wisdom into the executive's mind.Moreover, when the executive coach makes it clear not to expect miracles overnight, they take a stand against Executive coaching.Constant interactions with his executive coach led to Mark transforming into a bold decision maker who worked wonders for himself and his team.These interactions followed by hours of interaction between the coach and the leader; Eric through the same process with his Executive coach.