Learn to code like a professional with Python – an open source, versatile, and powerful programming language About This Book Learn the fundamentals of programming with Python – one of the best languages ever created Develop a strong set of programming skills that you will be able to express in any situation, on every platform, thanks to Python’s portability Create outstanding applications of all kind, from websites to *ing, and from GUIs to data science Who This Book Is For Python is the most popular introductory teaching language in U.S. top computer science universities, so if you are new to software development, or maybe you have little experience, and would like to start off on the right foot, then this language and this book are what you need. Its amazing design and portability will help you become productive regardless of the environment you choose to work with. What You Will Learn Get Python up and running on Windows, Mac, and Linux in no time Grasp the fundamental concepts of coding, along with the basics of data structures and control flow. Write elegant, reusable, and efficient code in any situation Understand when to use the functional or the object oriented programming approach Create bulletproof, reliable software by writing tests to support your code Explore examples of GUIs, *ing, data science and web applications Learn to be independent, capable of fetching any resource you need, as well as dig deeper In Detail Learning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application. The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned. Style and approach This book is an easy-to-follow guide that will take you from a novice to the proficient level at a comfortable pace, using a lot of simple but effective examples. Each topic is explained thoroughly, and pointers are left for the more inquisitive readers to dig deeper and expand their knowledge.

Discover all the secrets of Unreal Engine and create seven fully functional games with the help of step-by-step instructions About This Book Understand what a Blueprint is and how to create a complex visual *ing code Discover the infinite possibilities that Unreal Engine offers, and understand which tool to use, where and when Learn to think like a real game developer in order to create enjoyable and bug-free games using this comprehensive and practical handbook Who This Book Is For This book is ideal for intermediate level developers who know how to use Unreal Engine and want to go through a series of projects that will further their expertise. Working knowledge of C++ is a must. What You Will Learn Write clean and reusable Blueprint *s Develop any kind of game you have in mind, following the rules used by experts Move through Unreal Engine 4, always knowing what you are doing and where to find the right tool for your needs Integrate C++ code into your projects using Visual Studio and the tools that Unreal provides Extricate between classes, nodes, interfaces, macros, and functions Work with different types of assets, from 3D objects to audio sources, from UI buttons to animations Explore all the aspects of the game logic—collisions, navigation meshes, matinees, volumes, events, and states In Detail With the arrival of Unreal Engine 4, a new wonderful tool was born: Blueprint. This visual *ing tool allows even non-programmers to develop the logic for their games, allowing almost anyone to create entire games without the need to write a single line of code. The range of features you can access with Blueprint * is pretty extensive, making it one of the foremost choices for many game developers. Unreal Engine Game Development Blueprints helps you unleash the real power of Unreal by helping you to create engaging and spectacular games. It will explain all the aspects of developing a game, focusing on visual *ing, and giving you all the information you need to create your own games. We start with an introductory chapter to help you move fluidly inside the Blueprint user interface, recognize its different components, and understand any already written Blueprint *. Following this, you will learn how to modify generated Blueprint classes to produce a single player tic-tac-toe game and personalize it. Next, you will learn how to create simple user interfaces, and how to extend Blueprints through code. This will help you make an informed decision between choosing Blueprint or code. You will then see the real power of Unreal unleashed as you create a beautiful scene with moving, AI controlled objects, particles, and lights. Then, you will learn how to create AI using a behavior tree and a global level Blueprint, how to modify the camera, and how to shoot custom bullets. Finally, you will create a complex game using Blueprintable components complete with a menu, power-up, dangerous objects, and different weapons. Style and approach This is an easy-to-follow guide full of practical game examples. Each chapter contains step-by-step instructions to build a complete game and each game uses a different tool in order to cover all the topics in a detailed and progressive manner.

Over 60 recipes to help you create professional and exquisite UIs to make your games more immersive About This Book Design and develop interactive and professional user interfaces (UIs) for games in Unity Discover how to implement and deal with various in-game UI elements that will impress your players This practical recipe guide will help you to efficiently create powerful and remarkable UIs using C# code Who This Book Is For If you are a game developer with some experience in Unity and C# and want to create the best interactive experience fast and intuitively, then this book is for you. If you are an intermediate game developer or an expert, these recipes will help you bring out the power of the new UI Unity system. What You Will Learn Implement different kinds of counters and healthbars Deal with timers and find out how to format them Animate and vivify UI elements Handle runtime customizations Add complex Head-up displays (HUDs) Design and implement 3D UIs Integrate minimaps in the UI In Detail With the increasing interest in game development, it's essential to design and implement a UI that reflects the game settings and shows the right information to the player. The Unity system is used to create complex and aesthetically pleasing user interfaces in order to give a professional look and feel to a game. Although the new Unity UI system is powerful and quite easy to use, by integrating it with C# *s, it's possible to realize the potential of this system and bring an impressive UI to games.This guide is an invaluable collection of recipes if you are planning to use Unity to develop a game. Starting with the basic concepts of the UI components, we’ll take you all the way through to creating complex interfaces by including animations and dynamics elements. Based on real-world problems, these recipes will start by showing you how to make common UI elements such as counters and healthbars. You will then get a walkthrough of how to manage time using timers, and will learn how to format them. You will move on to decorating and animating the UI elements to vivify them and give them a professional touch. Furthermore, you will be guided into the 3D UI world and into HUD *ing. Finally, you will discover how to implement complex minimaps in the interface.Style and approach Interactive, easy-to-follow recipes will help you create and implement UIs that make gaming an exhilarating experience.

An effective guide to geographic information systems and remote sensing analysis using Python 3 About This Book Construct applications for GIS development by exploiting Python This focuses on built-in Python modules and libraries compatible with the Python Packaging Index distribution system—no compiling of C libraries necessary This practical, hands-on tutorial teaches you all about Geospatial analysis in Python Who This Book Is For If you are a Python developer, researcher, or analyst who wants to perform Geospatial, modeling, and GIS analysis with Python, then this book is for you. Familarity with digital mapping and analysis using Python or another *ing language for automation or crunching data manually is appreciated. What You Will Learn Automate Geospatial analysis workflows using Python Code the simplest possible GIS in 60 lines of Python Mold thematic maps with Python tools Get hold of the various forms that geospatial data comes in Produce elevation contours using Python tools Create flood inundation models Apply Geospatial analysis to find out about real-time data tracking and for storm chasing In Detail Geospatial Analysis is used in almost every field you can think of from medicine, to defense, to farming. This book will guide you gently into this exciting and complex field. It walks you through the building blocks of geospatial analysis and how to apply them to influence decision making using the latest Python software. Learning Geospatial Analysis with Python, 2nd Edition uses the expressive and powerful Python 3 programming language to guide you through geographic information systems, remote sensing, topography, and more, while providing a framework for you to approach geospatial analysis effectively, but on your own terms. We start by giving you a little background on the field, and a survey of the techniques and technology used. We then split the field into its component specialty areas: GIS, remote sensing, elevation data, advanced modeling, and real-time data. This book will teach you everything you need to know about, Geospatial Analysis from using a particular software package or API to using generic algorithms that can be applied. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don’t become bogged down in just getting ready to do analysis. This book will round out your technical library through handy recipes that will give you a good understanding of a field that supplements many a modern day human endeavors. Style and approach This is a practical, hands-on tutorial that teaches you all about Geospatial analysis interactively using Python.

Create succinct and expressive implementations with functional programming in Python About This Book ? Learn how to choose between imperative and functional approaches based on expressiveness, clarity, and performance ? Get familiar with complex concepts such as monads, concurrency, and immutability ? Apply functional Python to common Exploratory Data Analysis (EDA) programming problems Who This Book Is For This book is for Python developers who would like to perform Functional programming with Python. Python Programming knowledge is assumed. What You Will Learn ? Use Python's generator functions and generator expressions to work with collections in a non-strict (or lazy) manner ? Utilize Python library modules including itertools, functools, multiprocessing, and concurrent features to ensure efficient functional programs ? Use Python strings with object-oriented suffix notation and prefix notation ? Avoid stateful classes with families of tuples ? Design and implement decorators to create composite functions ? Use functions such as max(), min(), map(), filter(), and sorted() ? Write higher-order functions In Detail If you’re a Python developer who wants to discover how to take the power of functional programming (FP) and bring it into your own programs, then this book is essential for you, even if you know next to nothing about the paradigm. Starting with a general overview of functional concepts, you’ll explore common functional features such as first-class and higher-order functions, pure functions, and more. You’ll see how these are accomplished in Python 3.6 to give you the core foundations you’ll build upon. After that, you’ll discover common functional optimizations for Python to help your apps reach even higher speeds. You’ll learn FP concepts such as lazy evaluation using Python’s generator functions and expressions. Moving forward, you’ll learn to design and implement decorators to create composite functions. You'll also explore data preparation techniques and data exploration in depth, and see how the Python standard library fits the functional programming model. Finally, to top off your journey into the world of functional Python, you’ll at look at the PyMonad project and some larger examples to put everything into perspective. Style and approach This book provides a general overview of functional concepts and then delves deeper into the functional features, showing you how the Python standard library fits the functional programming model. It also demonstrates how to implement common functional programming design patterns and techniques in Python.

Spatial statistics has the potential to provide insight that is not otherwise available through traditional GIS tools. This book is designed to introduce you to the use of spatial statistics so you can solve complex geographic analysis. The book begins by introducing you to the many spatial statistics tools available in ArcGIS. You will learn how to analyze patterns, map clusters, and model spatial relationships with these tools. Further on, you will explore how to extend the spatial statistics tools currently available in ArcGIS, and use the R programming language to create custom tools in ArcGIS through the ArcGIS Bridge using real-world examples. At the end of the book, you will be presented with two exciting case studies where you will be able to practically apply all your learning to analyze and gain insights into real estate data. What you will learn ?Get to know how to measure geographic distributions ?Perform clustering analysis including hot spot and outlier analysis ?Conduct data conversion tasks using the Utilities toolset ?Understand how to use the tools provided by the Mapping Clusters toolset in the Spatial Statistics Toolbox ?Get to grips with the basics of R for performing spatial statistical programming ?Create custom ArcGIS tools with R and ArcGIS Bridge ?Understand the application of Spatial Statistics tools

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. What you will learn ?Get a solid understanding of the data processing toolbox available in Java ?Explore the data science ecosystem available in Java

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide About This Book ?Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow ?Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide ?Real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. What You Will Learn ?Learn about machine learning landscapes along with the historical development and progress of deep learning ?Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x ?Access public datasets and utilize them using TensorFlow to load, process, and transform data ?Use TensorFlow on real-world datasets, including images, text, and more ?Learn how to evaluate the performance of your deep learning models ?Using deep learning for scalable object detection and mobile computing ?Train machines quickly to learn from data by exploring reinforcement learning techniques ?Explore active areas of deep learning research and applications

Do you want to make sense of your data? Do you want to create interactive charts, data trees, info-graphics, geospatial charts, and maps efficiently? This book is your ideal choice to master interactive data visualization with D3.js V4. The book includes a number of extensive examples that to help you hone your skills with data visualization. Throughout nine chapters these examples will help you acquire a clear practical understanding of the various techniques, tools and functionality provided by D3.js. You will first setup your D3.JS development environment and learn the basic patterns needed to visualize your data. After that you will learn techniques to optimize different processes such as working with selections; animating data transitions; creating graps and charts, integrating external resources (static as well as streaming); visualizing information on maps; working with colors and scales; utilizing the different D3.js APIs; and much more. The book will also guide you through creating custom graphs and visualizations, and show you how to go from the raw data to beautiful visualizations. The extensive examples will include working with complex and realtime data streams, such as seismic data, geospatial data, scientific data, and more. Towards the end of the book, you will learn to add more functionality on top of D3.js by using it with other external libraries and integrating it with Ecma* 6 and Type*

Writing concurrent and parallel programming applications is an integral skill for any Java programmer. Java 9 comes with a host of fantastic features, including significant performance improvements and new APIs. This book will take you through all the new APIs, showing you how to build parallel and multi-threaded applications. The book covers all the elements of the Java Concurrency API, with essential recipes that will help you take advantage of the exciting new capabilities. You will learn how to use parallel and reactive streams to process massive data sets. Next, you will move on to create streams and use all their intermediate and terminal operations to process big collections of data in a parallel and functional way. Further, you ll discover a whole range of recipes for almost everything, such as thread management, synchronization, executors, parallel and reactive streams, and many more. At the end of the book, you will learn how to obtain information about the status of some of the most useful components of the Java Concurrency API and how to test concurrent applications using different tools. What you will learn ?Find out to manage the basic components of the Java Concurrency API ?Use synchronization mechanisms to avoid data race conditions and other problems of concurrent applications ?Separate the thread management from the rest of the application with the Executor framework ?Solve problems using a parallelized version of the divide and conquer paradigm with the Fork / Join framework ?Process massive data sets in an optimized way using streams and reactive streams ?See which data structures we can use in concurrent applications and how to use them ?Practice efficient techniques to test concurrent applications ?Get to know tips and tricks to design concurrent applications

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. What you will learn ?Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm ?Fine-tune a neural network to improve the quality of results ?Use deep learning for image and audio processing ?Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases ?Identify problems

Work through recipes to unlock the full potential of the next generation graphics API―Vulkan About This Book ?This book explores a wide range of modern graphics programming techniques and GPU compute methods to make the best use of the Vulkan API ?Learn techniques that can be applied to a wide range of platforms desktop, smartphones, and embedded devices Who This Book Is For This book is ideal for developers who know C/C++ languages, have some basic familiarity with graphics programming, and now want to take advantage of the new Vulkan API in the process of building next generation computer graphics. Some basic familiarity of Vulkan would be useful to follow the recipes. OpenGL developers who want to take advantage of the Vulkan API will also find this book useful. What You Will Learn?Work with Swapchain to present images on screen ?Create, submit, and synchronize operations processed by the hardware ?Create buffers and images, manage their memory, and upload data to them from CPU ?Explore de*or sets and set up an interface between application and shaders ?Organize drawing operations into a set of render passes and subpasses ?Prepare graphics pipelines to draw 3D scenes and compute pipelines to perform mathematical calculations ?Implement geometry projection and tessellation, texturing, lighting, and post-processing techniques ?Write shaders in GLSL and convert them into SPIR-V assemblies ?Find out about and

"Key Features ?Get to the grips with the latest version of Apache Spark ?Utilize Spark's machine learning library to implement predictive analytics ?Leverage Spark's powerful tools to load, analyze, clean, and transform your data Book De*ion Spark ML is the machine learning module of Spark. It uses in-memory RDDs to process machine learning models faster for clustering, classification, and regression. This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. What you will learn ?Get hands-on with the latest version of Spark ML ?Create your first Spark program with Scala and Python ?Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2 ?Access public machine learning datasets and use Spark to load, process, clean, and transform data ?Use Spark's machine learning library to implement programs by utilizing well-known machine learning models ?Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models ?Write Spark functions to evaluate the performance of your machine learning models "

Learn how to run new and old Windows applications in Docker containers. About This Book ? Package traditional .NET Frameworks apps and new .NET Core apps as Docker images, and run them in containers for increased efficiency, portability, and security ? Design and implement distributed applications that run across connected containers, using enterprise-grade open source software from public Docker images ? Build a full Continuous Deployment pipeline for a .NET Framework application, and deploy it to a highly-available Docker swarm running in the cloud Who This Book Is For If you want to modernize an old monolithic application without rewriting it, smooth the deployment to production, or move to DevOps or the cloud, then Docker is the enabler for you. This book gives you a solid grounding in Docker so you can confidently approach all of these scenarios. What You Will Learn ? Comprehend key Docker concepts: images, containers, registries, and swarms ? Run Docker on Windows 10, Windows Server 2016, and in the cloud ? Deploy and monitor distributed solutions across multiple Docker containers ? Run containers with high availability and fail-over with Docker Swarm ? Master security in-depth with the Docker platform, making your apps more secure ? Build a Continuous Deployment pipeline by running Jenkins in Docker ? Debug applications running in Docker containers using Visual Studio ? Plan the adoption of Docker in your own organization In Detail Docker is a platform for running server applications in lightweight units called containers. You can run Docker on Windows Server 2016 and Windows 10, and run your existing apps in containers to get significant improvements in efficiency, security, and portability. This book teaches you all you need to know about Docker on Windows, from 101 to deploying highly-available workloads in production. This book takes you on a Docker journey, starting with the key concepts and simple examples of how to run .NET Framework and .NET Core apps in Windows Docker containers. Then it moves on to more complex examples—using Docker to modernize the architecture and development of traditional ASP.NET and SQL Server apps. The examples show you how to break up monoliths into distributed apps and deploy them to a clustered environment in the cloud, using the exact same artifacts you use to run them locally. To help you move confidently to production, it then explains Docker security, and the management and support options. The book finishes with guidance on getting started with Docker in your own projects, together with some real-world case studies for Docker implementations, from small-scale on-premises apps to very large-scale apps running on Azure. Style and approach Using a step-by-step approach, this book shows you how to use Docker on Windows. It includes practical examples and real-world technical and business scenarios that will help you effectively implement Docker in your environment.There are over 50 examples of Dockerized applications, using C# .NET projects as the source and packaging them into Docker images.

Build cloud native applications in Python About This Book ? This is the only reliable resource that showcases the tools and techniques you need build robust and resilient cloud native applications in Python ? Learn how to architect your application on both, the AWS and Azure clouds for high availability ? Assess, monitor, and troubleshoot your applications in the cloud Who This Book Is For This book is ideal for developers with a basic knowledge of Python who want to learn to build, test, and scale their Python-based applications. No prior experience of writing microservices in Python is required. What You Will Learn ? Get to know “the way of the cloud”, including why developing good cloud software is fundamentally about mindset and discipline ? Know what microservices are and how to design them ? Create reactive applications in the cloud with third-party messaging providers ? Build massive-scale, user-friendly GUIs with React and Flux ? Secure cloud-based web applications: the do’s, don’ts, and options ? Plan cloud apps that support continuous delivery and deployment In Detail Businesses today are evolving so rapidly that having their own infrastructure to support their expansion is not feasible. As a result, they have been resorting to the elasticity of the cloud to provide a platform to build and deploy their highly scalable applications. This book will be the one stop for you to learn all about building cloud-native architectures in Python. It will begin by introducing you to cloud-native architecture and will help break it down for you. Then you’ll learn how to build microservices in Python using REST APIs in an event driven approach and you will build the web layer. Next, you’ll learn about Interacting data services and building Web views with React, after which we will take a detailed look at application security and performance. Then, you’ll also learn how to Dockerize your services. And finally, you’ll learn how to deploy the application on the AWS and Azure platforms. We will end the book by discussing some concepts and techniques around troubleshooting problems that might occur with your applications after you’ve deployed them. This book will teach you how to craft applications that are built as small standard units, using all the proven best practices and avoiding the usual traps. It's a practical book: we're going to build everything using Python 3 and its amazing tooling ecosystem. The book will take you on a journey, the destination of which, is the creation of a complete Python application based on microservices over the cloud platform Style and approach Filled with examples, this book takes a step-by-step approach to teach you each and every configuration you need to make your application highly available and fault tolerant.

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book ? Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. ? Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. ? Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn ? Acquaint yourself with important elements of Machine Learning ? Understand the feature selection and feature engineering process ? Assess performance and error trade-offs for Linear Regression ? Build a data model and understand how it works by using different types of algorithm ? Learn to tune the parameters of Support Vector machines ? Implement clusters to a dataset ? Explore the concept of Natural Processing Language and Recommendation Systems ? Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.

Build a variety of real-world applications by taking advantage of the newest features of Java 9 About This Book ? See some of the new features of Java 9 and be introduced to parts of the Java SDK ? This book provides a set of diverse, interesting projects that range in complexity from fairly simple to advanced and cover HTTP 2.0 ? Take advantage of Java's new modularity features to write real-world applications that solve a variety of problems Who This Book Is For This book is for Java developers who are already familiar with the language. Familiarity with more advanced topics, such as network programming and threads, would be helpful, but is not assumed. What You Will Learn ? Learn how to package Java applications as modules by using the Java Platform Module System ? Implement process management in Java by using the all-new process handling API ? Integrate your applications with third-party services in the cloud ? Interact with mail servers using JavaMail to build an application that filters spam messages ? Learn to use JavaFX to build rich GUI based applications, which are an essential element of application development ? Write microservices in Java using platform libraries and third-party frameworks ? Integrate a Java application with MongoDB to build a cloud-based note taking application In Detail Java is a powerful language that has applications in a wide variety of fields. From playing games on your computer to performing banking transactions, Java is at the heart of everything. The book starts by unveiling the new features of Java 9 and quickly walks you through the building blocks that form the basis of writing applications. There are 10 comprehensive projects in the book that will showcase the various features of Java 9. You will learn to build an email filter that separates spam messages from all your inboxes, a social media aggregator app that will help you efficiently track various feeds, and a microservice for a client/server note application, to name a few. The book covers various libraries and frameworks in these projects, and also introduces a few more frameworks that complement and extend the Java SDK. Through the course of building applications, this book will not only help you get to grips with the various features of Java 9, but will also teach you how to design and prototype professional-grade applications with performance and security considerations. Style and approach This is a learn-as-you-build practical guide to building full-fledged applications using Java 9. With a project-based approach, we’ll improve your Java skills. You will experience a variety of solutions to problems with Java.

Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniques About This Book Dive deeper into data mining with Python – don’t be complacent, sharpen your skills! From the most common elements of data mining to cutting-edge techniques, we’ve got you covered for any data-related challenge Become a more fluent and confident Python data-analyst, in full control of its extensive range of libraries Who This Book Is For This book is for data scientists who are already familiar with some basic data mining techniques such as SQL and machine learning, and who are comfortable with Python. If you are ready to learn some more advanced techniques in data mining in order to become a data mining expert, this is the book for you! What You Will Learn Explore techniques for finding frequent itemsets and association rules in large data sets Learn identification methods for entity matches across many different types of data Identify the basics of network mining and how to apply it to real-world data sets Discover methods for detecting the sentiment of text and for locating named entities in text Observe multiple techniques for automatically extracting summaries and generating topic models for text See how to use data mining to fix data anomalies and how to use machine learning to identify outliers in a data set In Detail Data mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy – without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding. If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries. In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get. By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics. Style and approach This book will teach you the intricacies in applying data mining using real-world scenarios and will act as a very practical solution to your data mining needs.

Over 80 recipes that will take your PHP 7 web development skills to the next level! About This Book This is the most up-to-date book in the market on PHP It covers the new features of version 7.x, best practices for server-side programming, and MVC frameworks The recipe-based approach will allow you to explore the unique capabilities that PHP offers to web programmers Who This Book Is For If you are an aspiring web developer, mobile developer, or backend programmer, then this book is for you as it will take your PHP programming skills to next level. Basic knowledge of PHP programming is assumed. What You Will Learn Use advanced PHP 7 features, such as the Abstract Syntax Tree, Uniform Variable Syntax, Scalar Type Hints, Generator Delegation, Anonymous Classes, and the Context Sensitive Lexer Discover where and when PHP 5 code needs to be re-written to avoid backwards-compatibility breaks Improve the overall application security and error handling by taking advantage of classes that implement the new throwable interface Solve practical real-world programming problems using PHP 7 Develop middle-wareclasses that allow PHP developers to gluedifferent open source libraries together seamlessly Define and Implement PSR-7 classes Create custom middleware using PSR-7 compliant classes Test and debug your code, and get to know the best practices In Detail PHP 7 comes with a myriad of new features and great tools to optimize your code and make your code perform faster than in previous versions. Most importantly, it allows you to maintain high traffic on your websites with low-cost hardware and servers through a multithreading web server. This book demonstrates intermediate to advanced PHP techniques with a focus on PHP 7. Each recipe is designed to solve practical, real-world problems faced by PHP developers like yourself every day. We also cover new ways of writing PHP code made possible only in version 7. In addition, we discuss backward-compatibility breaks and give you plenty of guidance on when and where PHP 5 code needs to be changed to produce the correct results when running under PHP 7. This book also incorporates the latest PHP 7.x features. By the end of the book, you will be equipped with the tools and skills required to deliver efficient applications for your websites and enterprises. Style and approach This book takes a recipe-based approach, with real-world examples that can serve as building blocks for a larger application. Each recipe is self-contained with no external dependencies. This book follows a problem-solution strategy so you understand how to deal with various scenarios you may encounter while using PHP 7 in your daily activities.

Build highly available and performant native web applications that seamlessly integrate with third-party APIs About This Book Get straight into the action with step-by-step recipes that show you how to put Service Workers to work Find out what Service Workers can do for your app, then do it! Get the first in-depth look at this important new feature for web developers Who This Book Is For Web developers, mobile application developers, and software engineers with any level of knowledge can use this book. You should be familiar with JavaScript and HTML. What You Will Learn Display a custom offline page Cache critical resources for offline use Implement offline Google Analytics Get network responses offline Implement push notifications Improve performance of your app In Detail It would be nice to have web apps that work offline and send push notifications. This is now possible with Service Workers, which can add native-like functionality to your web apps without requiring a download. This book will get your mobile and web apps functioning without Internet connectivity, improve performance and network interaction in order to increase the level of availability, and show you how to build performant applications that seamlessly integrate with third-party APIs. We’ll show you how to add Service Worker functionality to web apps and sites, access offline content through basic and advanced techniques, and build powerful interactive system notifications. We’ll also teach you about cache functionality and assets to provide immediate load even over narrow connections. We conclude by giving you various tips to improve app performance, including the background sync technique. By the end of this book, you’ll know build high performing and faster web and mobile applications with Service Workers. Style and approach This book provides lots of task-oriented, practical, and inspiring ways to put Service Workers to work. Step-by-step instructions will guide you through every task.

A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters About This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory. What You Will Learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data. Style and approach This step-by-step pragmatic guide will make life easy no matter what your level of experience. You will deep dive into Apache Spark on Hadoop clusters through ample exciting real-life examples. Practical tutorial explains data science in simple terms to help programmers and data analysts get started with Data Science