Big data
Mastering PostGIS. Modern ways to create, analyze, and implement spatial data
Dominik Mikiewicz, Michal Mackiewicz, Tomasz Nycz
PostGIS is open source extension onf PostgreSQL object-relational database system that allows GIS objects to be stored and allows querying for information and location services. The aim of this book is to help you master the functionalities offered by PostGIS- from data creation, analysis and output, to ETL and live edits.The book begins with an overview of the key concepts related to spatial database systems and how it applies to Spatial RMDS. You will learn to load different formats into your Postgres instance, investigate the spatial nature of your raster data, and finally export it using built-in functionalities or 3th party tools for backup or representational purposes. Through the course of this book, you will be presented with many examples on how to interact with the database using JavaScript and Node.js. Sample web-based applications interacting with backend PostGIS will also be presented throughout the book, so you can get comfortable with the modern ways of consuming and modifying your spatial data.
Mastering PostgreSQL 9.6. A comprehensive guide for PostgreSQL 9.6 developers and administrators
Hans-Jürgen Schönig
PostgreSQL is an open source database used for handling large datasets (Big Data) and as a JSON document database. It also has applications in the software and web domains. This book will enable you to build better PostgreSQL applications and administer databases more efficiently.We begin by explaining the advanced database design concepts in PostgreSQL 9.6, along with indexing and query optimization. You will also see how to work with event triggers and perform concurrent transactions and table partitioning, along with exploring SQL and server tuning. We will walk you through implementing advanced administrative tasks such as server maintenance and monitoring, replication, recovery and high availability, and much more. You will understand the common and not-so-common troubleshooting problems and how you can overcome them.By the end of this book, you will have an expert-level command of the advanced database functionalities and will be able to implement advanced administrative tasks with PostgreSQL.
Mastering Predictive Analytics with Python. Click here to enter text
Joseph Babcock
The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services.Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life
James D. Miller, Rui Miguel Forte
R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems.The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks.By the end of this book, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R.
Mastering Puppet 5. Optimize enterprise-grade environment performance with Puppet
Ryan Russell-Yates, Jason Southgate
Puppet is a configuration management system and a language written for and by system administrators to manage a large number of systems efficiently and prevent configuration drift.The core topics this book addresses are Puppet's latest features and mastering Puppet Enterprise. You will begin by writing a new Puppet module, gaining an understanding of the guidelines and style of the Puppet community. Following on from this, you will take advantage of the roles and profiles pattern, and you will learn how to structure your code. Next, you will learn how to extend Puppet and write custom facts, functions, types, and providers in Ruby, and also use the new features of Hiera 5. You will also learn how to configure the new Code Manager component, and how to ensure code is automatically deployed to (multiple) Puppet servers. Next, you will learn how to integrate Puppet with Jenkins and Git to build an effective workflow for multiple teams, and use the new Puppet Tasks feature and the latest Puppet Orchestrator language extensions. Finally, you will learn how to scale and troubleshoot Puppet.By the end of the book, you will be able to deal with problems of scale and exceptions in your code, automate workflows, and support multiple developers working simultaneously.
Luiz Felipe Martins, Gavin Hackeling, Magnus Vilhelm...
Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want!Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You’ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making.You’ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you’ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we’ll focus on performing regression using computational tools and you’ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you’ll discover how to use Python’s tools for supervised machine learning.
Kirthi Raman, Sebastian Cheung, Kirthi Venkatraman
Kamon Ayeva, Sakis Kasampalis
Python is an object-oriented scripting language that is used in a wide range of categories. In software engineering, a design pattern is an elected solution for solving software design problems. Although they have been around for a while, design patterns remain one of the top topics in software engineering, and are a ready source for software developers to solve the problems they face on a regular basis. This book takes you through a variety of design patterns and explains them with real-world examples. You will get to grips with low-level details and concepts that show you how to write Python code, without focusing on common solutions as enabled in Java and C++. You'll also fnd sections on corrections, best practices, system architecture, and its designing aspects. This book will help you learn the core concepts of design patterns and the way they can be used to resolve software design problems. You'll focus on most of the Gang of Four (GoF) design patterns, which are used to solve everyday problems, and take your skills to the next level with reactive and functional patterns that help you build resilient, scalable, and robust applications. By the end of the book, you'll be able to effciently address commonly faced problems and develop applications, and also be comfortable working on scalable and maintainable projects of any size.
Sushant Saurav, Hemant Kumar Mehta
Mastering PyTorch. Build powerful neural network architectures using advanced PyTorch 1.x features
Ashish Ranjan Jha, Dr. Gopinath Pillai
Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models.The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai.By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Martin Mahler, Juan Ignacio Vitantonio
Qlik Sense is a powerful, self-servicing Business Intelligence tool for data discovery, analytics and visualization. It allows you to create personalized Business Intelligence solutions from raw data and get actionable insights from it.This book is your one-stop guide to mastering Qlik Sense, catering to all your organizational BI needs. You'll see how you can seamlessly navigate through tons of data from multiple sources and take advantage of the various APIs available in Qlik and its components for guided analytics. You'll also learn how to embed visualizations into your existing BI solutions and extend the capabilities of Qlik Sense to create new visualizations and dashboards that work across all platforms. We also cover other advanced concepts such as porting your Qlik View applications to Qlik Sense,and working with Qlik Cloud. Finally, you'll implement enterprise-wide security and access control for resources and data sources through practical examples.With the knowledge gained from this book, you'll have become the go-to expert in your organization when it comes to designing BI solutions using Qlik Sense.
Edina Berlinger (EURO), Julia Molnár, Agnes Vidovics...
Shahid Shaikh
RethinkDB has a lot of cool things to be excited about: ReQL (its readable,highly-functional syntax), cluster management, primitives for 21st century applications, and change-feeds. This book starts with a brief overview of the RethinkDB architecture and data modeling, and coverage of the advanced ReQL queries to work with JSON documents. Then, you will quickly jump to implementing these conceptsin real-world scenarios, by building real-time applications on polling, data synchronization, share market, and the geospatial domain using RethinkDB andNode.js. You will also see how to tweak RethinkDB's capabilities to ensure faster data processing by exploring the sharding and replication techniques in depth.Then, we will take you through the more advanced administration tasks as well as show you the various deployment techniques using PaaS, Docker, and Compose. By the time you have finished reading this book, you would have taken your knowledge of RethinkDB to the next level, and will be able to use the concepts in RethinkDB to develop efficient, real-time applications with ease.
Alex Kozlov
Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
Vikram Garg, Sharan Kumar Ravindran
With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of standard statistical packages simplify analysis of the data.This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming.With this handy guide, you will be ready to embark on your journey as an independent social media analyst.
Duygu Altinok
spaCy is an industrial-grade, efficient NLP Python library. It offers various pre-trained models and ready-to-use features. Mastering spaCy provides you with end-to-end coverage of spaCy's features and real-world applications.You'll begin by installing spaCy and downloading models, before progressing to spaCy's features and prototyping real-world NLP apps. Next, you'll get familiar with visualizing with spaCy's popular visualizer displaCy. The book also equips you with practical illustrations for pattern matching and helps you advance into the world of semantics with word vectors. Statistical information extraction methods are also explained in detail. Later, you'll cover an interactive business case study that shows you how to combine all spaCy features for creating a real-world NLP pipeline. You'll implement ML models such as sentiment analysis, intent recognition, and context resolution. The book further focuses on classification with popular frameworks such as TensorFlow's Keras API together with spaCy. You'll cover popular topics, including intent classification and sentiment analysis, and use them on popular datasets and interpret the classification results.By the end of this book, you'll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create your own NLP apps.
Mastering Spark for Data Science. Lightning fast and scalable data science solutions
Andrew Morgan, Antoine Amend, Matthew Hallett, David...
Data science seeks to transform the world using data, and this is typically achievedthrough disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs.This book deep dives into using Spark to deliver production-grade data sciencesolutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more.You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly.
Marleen Meier, David Baldwin
Tableau is one of the leading business intelligence (BI) tools used to solve BI and analytics challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain. This book is also the second edition of the popular Mastering Tableau series, with new features, examples, and updated code. The book covers essential Tableau concepts and its advanced functionalities. Using Tableau Hyper and Tableau Prep, you’ll be able to handle and prepare data easily. You’ll gear up to perform complex joins, spatial joins, union, and data blending tasks using practical examples. Following this, you’ll learn how to perform data densification to make displaying granular data easier. Next, you’ll explore expert-level examples to help you with advanced calculations, mapping, and visual design using various Tableau extensions. With the help of examples, you’ll also learn about improving dashboard performance, connecting Tableau Server, and understanding data visualizations. In the final chapters, you’ll cover advanced use cases such as Self-Service Analytics, Time Series Analytics, and Geo-Spatial Analytics, and learn to connect Tableau to R, Python, and MATLAB. By the end of this book, you’ll have mastered the advanced offerings of Tableau and be able to tackle common and not-so-common challenges faced in the BI domain.
Marleen Meier, David Baldwin, Kate Strachnyi
Tableau is one of the leading business intelligence (BI) tools that can help you solve data analysis challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain.Updated with fresh topics including Quick Level of Detail expressions, the newest Tableau Server features, Einstein Discovery, and more, this book covers essential Tableau concepts and advanced functionalities. Leveraging Tableau Hyper files and using Prep Builder, you’ll be able to perform data preparation and handling easily. You’ll gear up to perform complex joins, spatial joins, unions, and data blending tasks using practical examples. Next, you’ll learn how to execute data densification and further explore expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions. You’ll also learn about improving dashboard performance, connecting to Tableau Server and understanding data visualization with examples. Finally, you'll cover advanced use cases such as self-service analysis, time series analysis, and geo-spatial analysis, and connect Tableau to Python and R to implement programming functionalities within it.By the end of this Tableau book, you’ll have mastered the advanced offerings of Tableau 2021 and be able to tackle common and advanced challenges in the BI domain.
Armando Fandango
TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs.This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images.You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected.The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems.
Mastering Text Mining with R. Extract and recognize your text data
Avinash Paul, KUMAR ASHISH
Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages.Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media.
Matematyka dyskretna dla praktyków. Algorytmy i uczenie maszynowe w Pythonie
Ryan T. White, Archana Tikayat Ray
Mimo że osiągnięcia matematyczne stały się podwalinami algorytmiki, wielu inżynierów nie w pełni rozumie reguły matematyki dyskretnej. Nawet jeśli nie stanowi to szczególnego problemu w codziennej pracy, w końcu okazuje się, że matematyka dyskretna jest niezbędna do osiągnięcia prawdziwej biegłości w operowaniu algorytmami i w pracy na danych. Co więcej, znajomość tej dziedziny bardzo ułatwia rozwiązywanie problemów z zakresu uczenia maszynowego. W ten sposób praktyczna biegłość w matematyce zauważalnie poprawia wyniki pracy inżynierów. Ta książka jest kompleksowym wprowadzeniem do matematyki dyskretnej, przydatnym dla każdego, kto chce pogłębić i ugruntować swoje umiejętności informatyczne. W zrozumiały sposób przedstawiono tu metody matematyki dyskretnej i ich zastosowanie w algorytmach i analizie danych, włączając w to techniki uczenia maszynowego. Zaprezentowano również zasady oceny złożoności obliczeniowej algorytmów i używania wyników tej oceny do zarządzania pracą procesora. Omówiono także sposoby przechowywania struktur grafowych, ich przeszukiwania i znajdywania ścieżek między wierzchołkami. Pokazano też, jak wykorzystać przedstawione informacje podczas posługiwania się bibliotekami Pythona, takimi jak scikit-learn i NumPy. W książce między innymi: terminologia i metody matematyki dyskretnej zastosowanie metod matematyki dyskretnej w algorytmach i analizie danych algebra Boole'a i kombinatoryka w podstawowych strukturach algorytmów rozwiązywanie problemów z dziedziny teorii grafów zadania związane z uczeniem maszynowym a matematyka dyskretna Matematyka dyskretna - poznaj, zrozum, zastosuj!
Hala Nelson
Sztuczna inteligencja i technologie oparte na danych są coraz częściej integrowane z istniejącymi systemami i operacjami. Ta tendencja dotyczy licznych branż. Dziś przy budowaniu systemów SI można korzystać z gotowych bibliotek, jeżeli jednak zależy Ci na w pełni świadomym tworzeniu doskonalszych aplikacji, musisz dobrze opanować matematykę leżącą u podstaw sztucznej inteligencji. Nawet jeśli nie darzysz królowej nauk płomiennym uczuciem, dzięki temu kompleksowemu opracowaniu z łatwością poradzisz sobie z jej lepszym poznaniem. Nie znajdziesz tu skomplikowanych teorii naukowych, tylko przystępnie podane koncepcje matematyczne niezbędne do rozwoju w dziedzinie sztucznej inteligencji, w szczególności do praktycznego stosowania najnowocześniejszych modeli. Poznasz takie zagadnienia jak regresja, sieci neuronowe, sieci konwolucyjne, optymalizacja, prawdopodobieństwo, procesy Markowa, równania różniczkowe i wiele innych w ekskluzywnym kontekście sztucznej inteligencji. Książkę docenią pasjonaci nowych technologii, twórcy aplikacji, inżynierowie i analitycy danych, a także matematycy i naukowcy. W książce: wyjaśnienie pojęć z zakresu uczenia maszynowego, inżynierii danych i matematyki ujednolicanie modeli w ramach jednej struktury matematycznej grafy i dane sieciowe eksploracja rzeczywistych danych, zmniejszanie liczby wymiarów i przetwarzanie obrazów korzystanie z modeli w różnych projektach opartych na danych implikacje i ograniczenia sztucznej inteligencji Ta książka w zachwycający sposób sprawia, że matematyka staje się zabawą dla licznych uczestników przyszłości opartej na sztucznej inteligencji! Adri Purkayastha, analityk oceny ryzyka, BNP Paribas O książce w mediach: Eksperyment Myślowy – recenzja książki