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As 7 Cores De Oníris

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As 7 Cores de Oníris

As 7 Cores de Oníris [Pdf/ePub] eBook Author: ,
Editor: Leya
ISBN-10: 9895556128
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As 7 Cores de Oníris by , Book Summary:

História de um mundo mágico... Em resposta à Guerra, uma cadeia montanhosa praticamente intransponível divide Oníris em sete partes, separando as sete raças humanas por cada um dos novos territórios. Reza a lenda que representantes das diferentes raças terão agora de se unir, ultrapassando as suas diferenças, para conseguir superar as provas de Deo - o deus criador - e recuperar a harmonia e a unidade do seu mundo. Bigo, Seara, Norma, Kanel e os gémeos Lyra e Aryl, nas suas aventuras, transportam-nos através de Oníris e dão-nos a conhecer as origens e as histórias deste mundo singular. Mas o grupo não poderá vencer o grande desafio enquanto elementos das sete raças não trabalharem juntos para o mesmo objectivo, e há forças que se movem para lhes dificultar essa tarefa. Os deuses estão atentos e tomam partido...

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Oníris - O Grande Desafio

Oníris - O Grande Desafio [Pdf/ePub] eBook Author: RITA VILELA
Editor: Leya
ISBN-10: 9895556322
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Oníris - O Grande Desafio by RITA VILELA Book Summary:

Reza a lenda que representantes das diferentes raças terão de cooperar entre si, ultrapassando as suas diferenças, para alcançar o território proibido e aí superar as provas de Deo - o deus criador -, libertando os Íris do seu cativeiro. Só assim Oniris voltará a ser um só! Neste segundo volume, Bigo, Seara, Norma, Kanel e os gémeos Lyra e Aryl foram separados e privados das suas recordações. Enfrentando perigos e obstáculos, terão agora de se reencontrar para concluir a missão que ficou pendente e tornar a lenda uma realidade. Só que, desta vez, não poderão contar com a ajuda da deusa. O destino de Oníris está nas mãos dos homens.

Download or read Oníris - O Grande Desafio book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Reza a lenda que representantes das diferentes raças terão de cooperar entre si, ultrapassando as suas diferenças, para alcançar o território proibido e aí superar as provas de Deo - o deus criador -, libertando os Íris do seu cativeiro. Só assim Oniris voltará a ser um só! Neste segundo volume, Bigo, Seara, Norma, Kanel e os gémeos Lyra e Aryl foram separados e privados das suas recordações. Enfrentando perigos e obstáculos, terão agora de se reencontrar para concluir a missão que ficou pendente e tornar a lenda uma realidade. Só que, desta vez, não poderão contar com a ajuda da deusa. O destino de Oníris está nas mãos dos homens.


Beyond the Point

Beyond the Point [Pdf/ePub] eBook Author: Claire Gibson
Editor: HarperCollins
ISBN-10: 0062853732
Size: 993 kb
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Beyond the Point by Claire Gibson Book Summary:

"An inspiring tribute to female friendship and female courage!"--Kate Quinn, New York Times bestselling author of The Alice Network and The Huntress. Three women are brought together in an enthralling story of friendship, heartbreak, and resilience. Set at the U.S. Military Academy at West Point, this is an amazing debut novel. Duty. Honor. Country. That’s West Point’s motto, and every cadet who passes through its stone gates vows to live it. But on the eve of 9/11, as Dani, Hannah and Avery face four grueling years ahead, they realize they’ll only survive if they do it together. Everyone knows Dani is going places. With athletic talent and a brilliant mind, she navigates West Point’s predominantly male environment with wit and confidence, breaking stereotypes and embracing new friends. Hannah’s grandfather, a legendary Army general, offers a stark warning about the dangers that lie ahead, but she moves forward anyway, letting faith guide her path. When she meets her soul mate at West Point, the future looks perfect, just as planned. Wild child Avery moves fast and doesn’t mind breaking a few rules (and hearts) along the way. But she can’t outpace her self-doubt, and the harder she tries, the further it leads her down a treacherous path. The world—of business, of love, and of war—awaits Dani, Hannah, and Avery beyond the gates of West Point. These three women know that what doesn’t kill you makes you stronger. But soon, that adage no longer rings true—for their future, or their friendship. As they’re pulled in different directions, will their hard-forged bond prevail or shatter? Beyond the Point is a heartfelt look at how our closest friends can become our fiercest battle buddies. After all, the greatest battles we fight rarely require a uniform.

Download or read Beyond the Point book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). "An inspiring tribute to female friendship and female courage!"--Kate Quinn, New York Times bestselling author of The Alice Network and The Huntress. Three women are brought together in an enthralling story of friendship, heartbreak, and resilience. Set at the U.S. Military Academy at West Point, this is an amazing debut novel. Duty. Honor. Country. That’s West Point’s motto, and every cadet who passes through its stone gates vows to live it. But on the eve of 9/11, as Dani, Hannah and Avery face four grueling years ahead, they realize they’ll only survive if they do it together. Everyone knows Dani is going places. With athletic talent and a brilliant mind, she navigates West Point’s predominantly male environment with wit and confidence, breaking stereotypes and embracing new friends. Hannah’s grandfather, a legendary Army general, offers a stark warning about the dangers that lie ahead, but she moves forward anyway, letting faith guide her path. When she meets her soul mate at West Point, the future looks perfect, just as planned. Wild child Avery moves fast and doesn’t mind breaking a few rules (and hearts) along the way. But she can’t outpace her self-doubt, and the harder she tries, the further it leads her down a treacherous path. The world—of business, of love, and of war—awaits Dani, Hannah, and Avery beyond the gates of West Point. These three women know that what doesn’t kill you makes you stronger. But soon, that adage no longer rings true—for their future, or their friendship. As they’re pulled in different directions, will their hard-forged bond prevail or shatter? Beyond the Point is a heartfelt look at how our closest friends can become our fiercest battle buddies. After all, the greatest battles we fight rarely require a uniform.


Introduction to Machine Learning with Python

Introduction to Machine Learning with Python [Pdf/ePub] eBook Author: Andreas C. Müller,Sarah Guido
Editor: \"O\'Reilly Media, Inc.\"
ISBN-10: 1449369898
Size: 968 kb
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Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido Book Summary:

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills

Download or read Introduction to Machine Learning with Python book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills


The Emerald Atlas

The Emerald Atlas [Pdf/ePub] eBook Author: John Stephens
Editor: Knopf Books for Young Readers
ISBN-10: 0375899553
Size: 2000 kb
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The Emerald Atlas by John Stephens Book Summary:

"A strong . . . new trilogy, invoking just a little Harry Potter and Series of Unfortunate Events along the way."—Realms of Fantasy Siblings Kate, Michael, and Emma have been in one orphanage after another for the last ten years, passed along like lost baggage. Yet these unwanted children are more remarkable than they could possibly imagine. Ripped from their parents as babies, they are being protected from a horrible evil of devastating power, an evil they know nothing about. Until now. Before long, Kate, Michael, and Emma are on a journey through time to dangerous and secret corners of the world . . . a journey of allies and enemies, of magic and mayhem. And—if an ancient prophesy is true—what they do can change history, and it's up to them to set things right. "A new Narnia for the tween set."—The New York Times "[A] fast-paced, fully imagined fantasy."—Publishers Weekly "Echoes of other popular fantasy series, from "Harry Potter" to the "Narnia" books, are easily found, but debut author Stephens has created a new and appealing read . . ."—School Library Journal, Starred Review

Download or read The Emerald Atlas book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). "A strong . . . new trilogy, invoking just a little Harry Potter and Series of Unfortunate Events along the way."—Realms of Fantasy Siblings Kate, Michael, and Emma have been in one orphanage after another for the last ten years, passed along like lost baggage. Yet these unwanted children are more remarkable than they could possibly imagine. Ripped from their parents as babies, they are being protected from a horrible evil of devastating power, an evil they know nothing about. Until now. Before long, Kate, Michael, and Emma are on a journey through time to dangerous and secret corners of the world . . . a journey of allies and enemies, of magic and mayhem. And—if an ancient prophesy is true—what they do can change history, and it's up to them to set things right. "A new Narnia for the tween set."—The New York Times "[A] fast-paced, fully imagined fantasy."—Publishers Weekly "Echoes of other popular fantasy series, from "Harry Potter" to the "Narnia" books, are easily found, but debut author Stephens has created a new and appealing read . . ."—School Library Journal, Starred Review


Statistics for Machine Learning

Statistics for Machine Learning [Pdf/ePub] eBook Author: Pratap Dangeti
Editor: Packt Publishing Ltd
ISBN-10: 1788291220
Size: 1583 kb
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Statistics for Machine Learning by Pratap Dangeti Book Summary:

Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.

Download or read Statistics for Machine Learning book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.


Introduction to Data Science

Introduction to Data Science [Pdf/ePub] eBook Author: Rafael A. Irizarry
Editor: CRC Press
ISBN-10: 1000707733
Size: 1310 kb
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Introduction to Data Science by Rafael A. Irizarry Book Summary:

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course.

Download or read Introduction to Data Science book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course.


R and Data Mining

R and Data Mining [Pdf/ePub] eBook Author: Yanchang Zhao
Editor: Academic Press
ISBN-10: 012397271X
Size: 766 kb
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R and Data Mining by Yanchang Zhao Book Summary:

R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation. With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work

Download or read R and Data Mining book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation. With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work


Metaphysics as a Guide to Morals

Metaphysics as a Guide to Morals [Pdf/ePub] eBook Author: Iris Murdoch
Editor: Random House
ISBN-10: 140904405X
Size: 457 kb
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Metaphysics as a Guide to Morals by Iris Murdoch Book Summary:

The decline of religion and ever increasing influence of science pose acute ethical issues for us all. Can we reject the literal truth of the Gospels yet still retain a Christian morality? Can we defend any 'moral values' against the constant encroachments of technology? Indeed, are we in danger of losing most of the qualities which make us truly human? Here, drawing on a novelists insight into art, literature and psychology, Iris Murdoch conducts an ongoing debate with major writers, thinkers and theologians - from Augustine to Wittgenstein, Shakespeare to Sartre, Plato to Derrida - to provide fresh and compelling answers to these crucial questions.

Download or read Metaphysics as a Guide to Morals book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). The decline of religion and ever increasing influence of science pose acute ethical issues for us all. Can we reject the literal truth of the Gospels yet still retain a Christian morality? Can we defend any 'moral values' against the constant encroachments of technology? Indeed, are we in danger of losing most of the qualities which make us truly human? Here, drawing on a novelists insight into art, literature and psychology, Iris Murdoch conducts an ongoing debate with major writers, thinkers and theologians - from Augustine to Wittgenstein, Shakespeare to Sartre, Plato to Derrida - to provide fresh and compelling answers to these crucial questions.


Practical Guide to Cluster Analysis in R

Practical Guide to Cluster Analysis in R [Pdf/ePub] eBook Author: Alboukadel Kassambara
Editor: STHDA
ISBN-10: 1542462703
Size: 1358 kb
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Practical Guide to Cluster Analysis in R by Alboukadel Kassambara Book Summary:

Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. Part I provides a quick introduction to R and presents required R packages, as well as, data formats and dissimilarity measures for cluster analysis and visualization. Part II covers partitioning clustering methods, which subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. Partitioning clustering approaches include: K-means, K-Medoids (PAM) and CLARA algorithms. In Part III, we consider hierarchical clustering method, which is an alternative approach to partitioning clustering. The result of hierarchical clustering is a tree-based representation of the objects called dendrogram. In this part, we describe how to compute, visualize, interpret and compare dendrograms. Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Among the chapters covered here, there are: Assessing clustering tendency, Determining the optimal number of clusters, Cluster validation statistics, Choosing the best clustering algorithms and Computing p-value for hierarchical clustering. Part V presents advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering.

Download or read Practical Guide to Cluster Analysis in R book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. Part I provides a quick introduction to R and presents required R packages, as well as, data formats and dissimilarity measures for cluster analysis and visualization. Part II covers partitioning clustering methods, which subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. Partitioning clustering approaches include: K-means, K-Medoids (PAM) and CLARA algorithms. In Part III, we consider hierarchical clustering method, which is an alternative approach to partitioning clustering. The result of hierarchical clustering is a tree-based representation of the objects called dendrogram. In this part, we describe how to compute, visualize, interpret and compare dendrograms. Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Among the chapters covered here, there are: Assessing clustering tendency, Determining the optimal number of clusters, Cluster validation statistics, Choosing the best clustering algorithms and Computing p-value for hierarchical clustering. Part V presents advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering.


Python Data Science Handbook

Python Data Science Handbook [Pdf/ePub] eBook Author: Jake VanderPlas
Editor: \"O\'Reilly Media, Inc.\"
ISBN-10: 1491912138
Size: 911 kb
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Python Data Science Handbook by Jake VanderPlas Book Summary:

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Download or read Python Data Science Handbook book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms


Machine Learning for Audio, Image and Video Analysis

Machine Learning for Audio, Image and Video Analysis [Pdf/ePub] eBook Author: Francesco Camastra,Alessandro Vinciarelli
Editor: Springer
ISBN-10: 144716735X
Size: 1487 kb
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Machine Learning for Audio, Image and Video Analysis by Francesco Camastra,Alessandro Vinciarelli Book Summary:

This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.

Download or read Machine Learning for Audio, Image and Video Analysis book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.


Data Science and Analytics

Data Science and Analytics [Pdf/ePub] eBook Author: Usha Batra,Nihar Ranjan Roy,Brajendra Panda
Editor: Springer Nature
ISBN-10: 9811558302
Size: 930 kb
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Data Science and Analytics by Usha Batra,Nihar Ranjan Roy,Brajendra Panda Book Summary:

This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics.

Download or read Data Science and Analytics book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics.


Advances in Communication, Signal Processing, VLSI, and Embedded Systems

Advances in Communication, Signal Processing, VLSI, and Embedded Systems [Pdf/ePub] eBook Author: Shubhakar Kalya,Muralidhar Kulkarni,K.S. Shivaprakasha
Editor: Springer Nature
ISBN-10: 9811506264
Size: 939 kb
Format File: Pdf
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Advances in Communication, Signal Processing, VLSI, and Embedded Systems by Shubhakar Kalya,Muralidhar Kulkarni,K.S. Shivaprakasha Book Summary:

This book comprises selected peer-reviewed papers from the International Conference on VLSI, Signal Processing, Power Systems, Illumination and Lighting Control, Communication and Embedded Systems (VSPICE-2019). The contents are divided into five broad topics - VLSI and embedded systems, signal processing, power systems, illumination and control, and communication and networking. The book focuses on the latest innovations, trends, and challenges encountered in the different areas of electronics and communication, and electrical engineering. It also offers potential solutions and provides an insight into various emerging areas such as image fusion, bio-sensors, and underwater sensor networks. This book can prove to be useful for academics and professionals interested in the various sub-fields of electronics and communication engineering.

Download or read Advances in Communication, Signal Processing, VLSI, and Embedded Systems book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This book comprises selected peer-reviewed papers from the International Conference on VLSI, Signal Processing, Power Systems, Illumination and Lighting Control, Communication and Embedded Systems (VSPICE-2019). The contents are divided into five broad topics - VLSI and embedded systems, signal processing, power systems, illumination and control, and communication and networking. The book focuses on the latest innovations, trends, and challenges encountered in the different areas of electronics and communication, and electrical engineering. It also offers potential solutions and provides an insight into various emerging areas such as image fusion, bio-sensors, and underwater sensor networks. This book can prove to be useful for academics and professionals interested in the various sub-fields of electronics and communication engineering.


Local Regression and Likelihood

Local Regression and Likelihood [Pdf/ePub] eBook Author: Clive Loader
Editor: Springer Science & Business Media
ISBN-10: 0387227326
Size: 429 kb
Format File: Pdf
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Local Regression and Likelihood by Clive Loader Book Summary:

Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation. Practical information is also included on how to implement these methods in the programs S-PLUS and LOCFIT.

Download or read Local Regression and Likelihood book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation. Practical information is also included on how to implement these methods in the programs S-PLUS and LOCFIT.


Python Machine Learning

Python Machine Learning [Pdf/ePub] eBook Author: Sebastian Raschka
Editor: Packt Publishing Ltd
ISBN-10: 1783555149
Size: 372 kb
Format File: Pdf
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Python Machine Learning by Sebastian Raschka Book Summary:

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Download or read Python Machine Learning book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.


R for Data Science

R for Data Science [Pdf/ePub] eBook Author: Hadley Wickham,Garrett Grolemund
Editor: \"O\'Reilly Media, Inc.\"
ISBN-10: 1491910348
Size: 1948 kb
Format File: Pdf
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R for Data Science by Hadley Wickham,Garrett Grolemund Book Summary:

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 analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Download or read R for Data Science book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). 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 analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results


The Story of Edgar Sawtelle

The Story of Edgar Sawtelle [Pdf/ePub] eBook Author: David Wroblewski
Editor: Bond Street Books
ISBN-10: 0307371891
Size: 981 kb
Format File: Pdf
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The Story of Edgar Sawtelle by David Wroblewski Book Summary:

A riveting family saga, The Story of Edgar Sawtelle explores the deep and ancient alliance between humans and dogs, and the power of fate through one boy’s epic journey into the wild. Born mute, speaking only in sign, Edgar Sawtelle leads an idyllic life with his parents on their farm in remote northern Wisconsin. For generations, the Sawtelles have raised and trained a fictional breed of dog whose thoughtful companionship is epitomized by Almondine, Edgar's lifelong companion. But with the unexpected return of Claude, Edgar's uncle, turmoil consumes the Sawtelle's once-peaceful home. When Edgar's father dies suddenly, Claude insinuates himself into the life of the farm – and into Edgar's mother’s affections. Grief-stricken and bewildered, Edgar tries to prove Claude played a role in his father's death, but his plan backfires, spectacularly. Edgar flees into the vast wilderness lying beyond the farm. He comes of age in the wild, fighting for his survival and that of the three yearling dogs who follow him. But his need to face his father’s murderer, and his devotion to the Sawtelle dogs, turn Edgar ever homeward. Wroblewski is a master storyteller, and his breathtaking scenes – the elemental north woods, the sweep of seasons, an iconic American barn, a ghost made of falling rain – create a family saga that is at once a brilliantly inventive retelling of Hamlet, an exploration of the limits of language, and a compulsively readable modern classic.

Download or read The Story of Edgar Sawtelle book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). A riveting family saga, The Story of Edgar Sawtelle explores the deep and ancient alliance between humans and dogs, and the power of fate through one boy’s epic journey into the wild. Born mute, speaking only in sign, Edgar Sawtelle leads an idyllic life with his parents on their farm in remote northern Wisconsin. For generations, the Sawtelles have raised and trained a fictional breed of dog whose thoughtful companionship is epitomized by Almondine, Edgar's lifelong companion. But with the unexpected return of Claude, Edgar's uncle, turmoil consumes the Sawtelle's once-peaceful home. When Edgar's father dies suddenly, Claude insinuates himself into the life of the farm – and into Edgar's mother’s affections. Grief-stricken and bewildered, Edgar tries to prove Claude played a role in his father's death, but his plan backfires, spectacularly. Edgar flees into the vast wilderness lying beyond the farm. He comes of age in the wild, fighting for his survival and that of the three yearling dogs who follow him. But his need to face his father’s murderer, and his devotion to the Sawtelle dogs, turn Edgar ever homeward. Wroblewski is a master storyteller, and his breathtaking scenes – the elemental north woods, the sweep of seasons, an iconic American barn, a ghost made of falling rain – create a family saga that is at once a brilliantly inventive retelling of Hamlet, an exploration of the limits of language, and a compulsively readable modern classic.


Book of Abstracts of the 68th Annual Meeting of the European Federation of Animal Science

Book of Abstracts of the 68th Annual Meeting of the European Federation of Animal Science [Pdf/ePub] eBook Author: EAAP Scientific committee
Editor: Wageningen Academic Publishers
ISBN-10: 9086868592
Size: 926 kb
Format File: Pdf
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Book of Abstracts of the 68th Annual Meeting of the European Federation of Animal Science by EAAP Scientific committee Book Summary:

This Book of Abstracts is the main publication of the 68th Annual Meeting of the European Federation of Animal Science (EAAP). It contains abstracts of the invited papers and contributed presentations of the sessions of EAAP's eleven Commissions: Animal Genetics, Animal Nutrition, Animal Management and Health, Animal Physiology, Cattle Production, Sheep and Goat Production, Pig Production, Horse Production and Livestock Farming Systems, Insects and Precision Livestock Farming.

Download or read Book of Abstracts of the 68th Annual Meeting of the European Federation of Animal Science book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This Book of Abstracts is the main publication of the 68th Annual Meeting of the European Federation of Animal Science (EAAP). It contains abstracts of the invited papers and contributed presentations of the sessions of EAAP's eleven Commissions: Animal Genetics, Animal Nutrition, Animal Management and Health, Animal Physiology, Cattle Production, Sheep and Goat Production, Pig Production, Horse Production and Livestock Farming Systems, Insects and Precision Livestock Farming.


Electromagnetic Waves and Lasers

Electromagnetic Waves and Lasers [Pdf/ePub] eBook Author: Wayne D. Kimura
Editor: Morgan & Claypool Publishers
ISBN-10: 168174614X
Size: 1771 kb
Format File: Pdf
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Electromagnetic Waves and Lasers by Wayne D. Kimura Book Summary:

This book reviews basic electromagnetic (EM) wave theory and applies it specifically to lasers in order to give the reader not only tangible examples of how the theory is manifested in real life, but also practical knowledge about lasers, and their operation and usage. The latter can be useful for those involved with using lasers. As a short treatise on this subject matter, this book is not intended to dwell deeply into the details of EM waves nor lasers. A bibliography is provided for those who wish to explore in more depth the topics covered in this book. Rather the aim of this book is to offer a quick overview, which will allow the reader to gain a competent general understanding of EM waves and lasers.

Download or read Electromagnetic Waves and Lasers book by clicking button below to visit the book download website. There are multiple format available for you to choose (Pdf, ePub, Doc). This book reviews basic electromagnetic (EM) wave theory and applies it specifically to lasers in order to give the reader not only tangible examples of how the theory is manifested in real life, but also practical knowledge about lasers, and their operation and usage. The latter can be useful for those involved with using lasers. As a short treatise on this subject matter, this book is not intended to dwell deeply into the details of EM waves nor lasers. A bibliography is provided for those who wish to explore in more depth the topics covered in this book. Rather the aim of this book is to offer a quick overview, which will allow the reader to gain a competent general understanding of EM waves and lasers.