Explore the world of data science from scratch with Julia by your side About This Book An in-depth exploration of Julia's growing ecosystem of packages Work with the most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn about deep learning using Mocha.jl and give speed and high performance to data analysis on large data sets Who This Book Is For This book is aimed at data analysts and aspiring data scientists who have a basic knowledge of Julia or are completely new to it. The book also appeals to those competent in R and Python and wish to adopt Julia to improve their skills set in Data Science. It would be beneficial if the readers have a good background in statistics and computational mathematics. What You Will Learn Apply statistical models in Julia for data-driven decisions Understanding the process of data munging and data preparation using Julia Explore techniques to visualize data using Julia and D3 based packages Using Julia to create self-learning systems using cutting edge machine learning algorithms Create supervised and unsupervised machine learning systems using Julia. Also, explore ensemble models Build a recommendation engine in Julia Dive into Julia’s deep learning framework and build a system using Mocha.jl In Detail Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia. Style and approach This practical and easy-to-follow yet comprehensive guide will get you learning about Julia with respect to data science. Each topic is explained thoroughly and placed in context. For the more inquisitive, we dive deeper into the language and its use case. This is the one true guide to working with Julia in data science.

Master powerful strategies to acquire and analyze evidence from real-life scenarios About This Book A straightforward guide to address the roadblocks face when doing mobile forensics Simplify mobile forensics using the right mix of methods, techniques, and tools Get valuable advice to put you in the mindset of a forensic professional, regardless of your career level or experience Who This Book Is For This book is for forensic analysts and law enforcement and IT security officers who have to deal with digital evidence as part of their daily job. Some basic familiarity with digital forensics is assumed, but no experience with mobile forensics is required. What You Will Learn Understand the challenges of mobile forensics Grasp how to properly deal with digital evidence Explore the types of evidence available on iOS, Android, Windows, and BlackBerry mobile devices Know what forensic outcome to expect under given circumstances Deduce when and how to apply physical, logical, over-the-air, or low-level (advanced) acquisition methods Get in-depth knowledge of the different acquisition methods for all major mobile platforms Discover important mobile acquisition tools and techniques for all of the major platforms In Detail Investigating digital media is impossible without forensic tools. Dealing with complex forensic problems requires the use of dedicated tools, and even more importantly, the right strategies. In this book, you’ll learn strategies and methods to deal with information stored on smartphones and tablets and see how to put the right tools to work. We begin by helping you understand the concept of mobile devices as a source of valuable evidence. Throughout this book, you will explore strategies and "plays" and decide when to use each technique. We cover important techniques such as seizing techniques to shield the device, and acquisition techniques including physical acquisition (via a USB connection), logical acquisition via data backups, over-the-air acquisition. We also explore cloud analysis, evidence discovery and data analysis, tools for mobile forensics, and tools to help you discover and analyze evidence. By the end of the book, you will have a better understanding of the tools and methods used to deal with the challenges of acquiring, preserving, and extracting evidence stored on smartphones, tablets, and the cloud. Style and approach This book takes a unique strategy-based approach, executing them on real-world scenarios. You will be introduced to thinking in terms of "game plans," which are essential to succeeding in analyzing evidence and conducting investigations.

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.

Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0 About This Book Perform data analysis and build predictive models on huge datasets that leverage Apache Spark Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges Work through practical examples on real-world problems with sample code snippets Who This Book Is For This book is for anyone who wants to leverage Apache Spark for data science and machine learning. If you are a technologist who wants to expand your knowledge to perform data science operations in Spark, or a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience who wants to learn about Big Data Analytics, this book is for you! What You Will Learn Consolidate, clean, and transform your data acquired from various data sources Perform statistical analysis of data to find hidden insights Explore graphical techniques to see what your data looks like Use machine learning techniques to build predictive models Build scalable data products and solutions Start programming using the RDD, DataFrame and Dataset APIs Become an expert by improving your data analytical skills In Detail This is the era of Big Data. The words ‘Big Data’ implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages. Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R. With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects. Style and approach This book takes a step-by-step approach to statistical analysis and machine learning, and is explained in a conversational and easy-to-follow style. Each topic is explained sequentially with a focus on the fundamentals as well as the advanced concepts of algorithms and techniques. Real-world examples with sample code snippets are also included.

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you'll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets. What you will learn ?Gain deep insights into the application of machine learning tools in the industry ?Manipulate data in R efficiently to prepare it for analysis ?Master the skill of recognizing techniques for effective visualization of data ?Understand why and how to create test and training data sets for analysis ?Master fundamental learning methods such as linear and logistic regression ?Comprehend advanced learning methods such as support vector

Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection. This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK. Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets. What you will learn ?Learn how to use the Amazon Machine Learning service from scratch for predictive analytics ?Gain hands-on experience of key Data Science concepts ?Solve classic regression and classification problems ?Run projects programmatically via the command line and the Python SDK

If you want to design and develop enterprise applications using .NET Core as the development framework and learn about industry-wide best practices and guidelines, then this book is for you. The book starts with a brief introduction to enterprise architecture, which will help you to understand what enterprise architecture is and what the key components are. It will then teach you about the types of patterns and the principles of software development, and explain the various aspects of distributed computing to keep your applications effective and scalable. These chapters act as a catalyst to start the practical implementation, and design and develop applications using different architectural approaches, such as layered architecture, service oriented architecture, microservices and cloud-specific solutions. Gradually, you will learn about the different approaches and models of the Security framework and explore various authentication models and authorization techniques, such as social media-based authentication and safe storage using app secrets. By the end of the book, you will get to know the concepts and usage of the emerging fields, such as DevOps, BigData, architectural practices, and Artificial Intelligence.

"Key Features ?Identify design issues and make the necessary adjustments to achieve improved performance ?Understand practical architectural quality attributes from the perspective of a practicing engineer and architect using Python ?Gain knowledge of architectural principles and how they can be used to provide accountability and rationale for architectural decisions Book De*ion This book starts off by explaining how Python fits into an application architecture. As you move along, you will understand the architecturally significant demands and how to determine them. Later, you'll get a complete understanding of the different architectural quality requirements that help an architect to build a product that satisfies business needs, such as maintainability/reusability, testability, scalability, performance, usability, and security. You will use various techniques such as incorporating DevOps, Continuous Integration, and more to make your application robust. You will understand when and when not to use object orientation in your applications. You will be able to think of the future and design applications that can scale proportionally to the growing business. The focus is on building the business logic based on the business process documentation and which frameworks are to be used when. We also cover some important patterns that are to be taken into account while solving design problems as well as those in relatively new domains such as the Cloud. This book will help you understand the ins and outs of Python so that you can make those critical design decisions that not just live up to but also surpass the expectations of your clients. What you will learn ?Build programs with the right architectural attributes ?Use Enterprise Architectural Patterns to solve scalable problems on the Web ?Understand design patterns from a Python perspective ?Optimize the performance testing tools in Python ?Deploy code in remote environments or on the Cloud using Python ?Secure architecture applications in Python About the Author Anand Balachandran Pillai is an Engineering and Technology professional with over 18 years of experience in the software industry in Product Engineering, Software Design & Architecture and Research. He has a Bachelor's degree in Mechanical Engineering from the Indian Institute of Technology, Madras. He has worked at companies such as Yahoo!, McAfee, and Infosys in the roles of Lead Engineer and Architect in product development teams, to build new products. His interests lie in Software Performance Engineering, High Scalability Architectures, Security and Open source communities. He often works with startups in lead technical or consulting role. He is the founder of the Bangalore Python Users Group and a Fellow of the Python Software Foundation (PSF). Anand is currently working as Senior Architect of Yegii Inc. "

Over 60 high-quality recipes covering debugging, security, performance, microservices, web frameworks, databases, deployment and more; rewritten for Node 4, 6, and 8. About This Book ? Security between Node.js and browser applications explained and applied in depth ? Cutting edge techniques and tools for measuring and improving performance ? Contemporary techniques to create developer-ergonomic, readily-scalable production systems Who This Book Is For If you have good knowledge of JavaScript and want to build fast, efficient, scalable client-server solutions, then this book is for you. Some experience with Node.js is assumed to get the most out of this book. If working from a beginner level Node Cookbook 2nd Edition is recommended as a primer for Node Cookbook 3rd Edition. What You Will Learn ? Rapidly become proficient at debugging Node.js programs ? Write and publish your own Node.js modules ? Become deeply acquainted with Node.js core API’s ? Use web frameworks such as Express, Hapi and Koa for accelerated web application development ? Apply Node.js streams for low-footprint infinite-capacity data processing ? Fast-track performance knowledge and optimization abilities ? Compare and contrast various persistence strategies, including database integrations with MongoDB, MySQL/MariaDB, Postgres, Redis, and LevelDB ? Grasp and apply critically essential security concepts ? Understand how to use Node with best-of-breed deployment technologies: Docker, Kubernetes and AWS In Detail The principles of asynchronous event-driven programming are perfect for today's web, where efficient real-time applications and scalability are at the forefront. Server-side JavaScript has been here since the 90s but Node got it right. This edition is a complete rewrite of the original, and is targeted against Node 4, 6, and 8. It shows you how to build fast, efficient, and scalable client-server solutions using the latest versions of Node. Beginning with adopting debugging tips and tricks of the trade and learning how to write your own modules, then covering the fundamentals of streams in Node.js, you will go on to discover I/O control, implementation of various web protocols, you’ll work up to integrating with cross-section of databases such as MongoDB, MySQL/MariaDB, Postgres, Redis, and LevelDB and building web application with Express, Hapi and Koa. You will then learn about security essentials in Node.js and the advanced optimization tools and techniques By the end of the book you should have acquired a level of proficiency that allows you to confidently build a full production-ready and scalable Node.js system. Style and approach This recipe-based practical guide presents each topic with step-by-step instructions on how you can create fast and efficient server side applications using the latest features and capabilities in Node 8 whilst also supporting usage with Node 4 and 6.

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book ? Load, store, edit, and visualize data using OpenCV and Python ? Grasp the fundamental concepts of classification, regression, and clustering ? Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide ? Evaluate, compare, and choose the right algorithm for any task Who This Book Is For This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks. What You Will Learn ? Explore and make effective use of OpenCV's machine learning module ? Learn deep learning for computer vision with Python ? Master linear regression and regularization techniques ? Classify objects such as flower species, handwritten digits, and pedestrians ? Explore the effective use of support vector machines, boosted decision trees, and random forests ? Get acquainted with neural networks and Deep Learning to address real-world problems ? Discover hidden structures in your data using k-means clustering ? Get to grips with data pre-processing and feature engineering In Detail Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch! Style and approach OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.

Master the principles to make applications robust, scalable and responsive About This Book ? Implement concurrent applications using the Java 9 Concurrency API and its new components ? Improve the performance of your applications and process more data at the same time, taking advantage of all of your resources ? Construct real-world examples related to machine learning, data mining, natural language processing, and more Who This Book Is For This book is for competent Java developers who have basic understanding of concurrency, but knowledge of effective implementation of concurrent programs or usage of streams for making processes more efficient is not required What You Will Learn ? Master the principles that every concurrent application must follow ? See how to parallelize a sequential algorithm to obtain better performance without data inconsistencies and deadlocks ? Get the most from the Java Concurrency API components ? Separate the thread management from the rest of the application with the Executor component ? Execute phased-based tasks in an efficient way with the Phaser components ? Solve problems using a parallelized version of the divide and conquer paradigm with the Fork / Join framework ? Find out how to use parallel Streams and Reactive Streams ? Implement the “map and reduce” and “map and collect” programming models ? Control the concurrent data structures and synchronization mechanisms provided by the Java Concurrency API ? Implement efficient solutions for some actual problems such as data mining, machine learning, and more In Detail Concurrency programming allows several large tasks to be divided into smaller sub-tasks, which are further processed as individual tasks that run in parallel. Java 9 includes a comprehensive API with lots of ready-to-use components for easily implementing powerful concurrency applications, but with high flexibility so you can adapt these components to your needs. The book starts with a full de*ion of the design principles of concurrent applications and explains how to parallelize a sequential algorithm. You will then be introduced to Threads and Runnables, which are an integral part of Java 9's concurrency API. You will see how to use all the components of the Java concurrency API, from the basics to the most advanced techniques, and will implement them in powerful real-world concurrency applications. The book ends with a detailed de*ion of the tools and techniques you can use to test a concurrent Java application, along with a brief insight into other concurrency mechanisms in JVM. Style and approach This is a complete guide that implements real-world examples of algorithms related to machine learning, data mining, and natural language processing in client/server environments. All the examples are explained using a step-by-step approach.

Unleash the data processing and analytics capability of Apache Spark with the language of choice: Java About This Book ? Perform big data processing with Spark—without having to learn Scala! ? Use the Spark Java API to implement efficient enterprise-grade applications for data processing and analytics ? Go beyond mainstream data processing by adding querying capability, Machine Learning, and graph processing using Spark Who This Book Is For If you are a Java developer interested in learning to use the popular Apache Spark framework, this book is the resource you need to get started. Apache Spark developers who are looking to build enterprise-grade applications in Java will also find this book very useful. What You Will Learn ? Process data using different file formats such as XML, JSON, CSV, and plain and delimited text, using the Spark core Library. ? Perform analytics on data from various data sources such as Kafka, and Flume using Spark Streaming Library ? Learn SQL schema creation and the analysis of structured data using various SQL functions including Windowing functions in the Spark SQL Library ? Explore Spark Mlib APIs while implementing Machine Learning techniques to solve real-world problems ? Get to know Spark GraphX so you understand various graph-based analytics that can be performed with Spark In Detail Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone. The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications. Style and approach This practical guide teaches readers the fundamentals of the Apache Spark framework and how to implement components using the Java language. It is a unique blend of theory and practical examples, and is written in a way that will gradually build your knowledge of Apache Spark.

Exploit the features of TypeScript to easily create your very own web applications About This Book ? Develop modular, scalable, maintainable, and adaptable web applications by taking advantage of TypeScript ? Walk through the fundamentals of TypeScript with the help of practical examples ? Enhance your web development skills using TypeScript 2.x Who This Book Is For If you are a developer aiming to learn TypeScript to build attractive web applications, this book is for you. No prior knowledge of TypeScript is required. However, a basic understanding of JavaScript would be an added advantage. What You Will Learn ? Understand TypeScript in depth, including its runtime and advanced type system features ? Master the core principles of the object-oriented programming and functional programming paradigms with TypeScript ? Save time using automation tools such as Gulp, Webpack, ts-node, and npm scripts ? Develop robust, modular, scalable, maintainable, and adaptable applications with testing frameworks such as Mocha, Chai, and Sinon.JS ? Put your TypeScript skills to practice by developing full-stack web applications with Node.js, React and Angular ? Use the APIs of the TypeScript compiler to build custom code analysis tool In Detail TypeScript is an open source and cross-platform statically typed superset of JavaScript that compiles to plain JavaScript and runs in any browser or host. This book is a step-by-step guide that will take you through the use and benefits of TypeScript with the help of practical examples. You will start off by understanding the basics as well as the new features of TypeScript 2.x. Then, you will learn how to work with functions and asynchronous programming APIs. You will continue by learning how to resolve runtime issues and how to implement TypeScript applications using the Object-oriented programming (OOP) and functional programming (FP) paradigms. Later, you will automate your development workflow with the help of tools such as Webpack. Towards the end of this book, you will delve into some real-world scenarios by implementing some full-stack TypeScript applications with Node.js, React and Angular as well as how to optimize and test them. Finally, you will be introduced to the internal APIs of the TypeScript compiler, and you will learn how to create custom code analysis tools. Style and approach This is a step-by-step guide that covers the fundamentals of TypeScript with practical examples.

A complete guide on using data structures and algorithms to write sophisticated C# code About This Book ? Master array, set and map with trees and graphs, among other fundamental data structures ? Delve into effective design and implementation techniques to meet your software requirements ? Explore illustrations to present data structures and algorithms, as well as their analysis in a clear, visual manner. Who This Book Is For This book is for developers who would like to learn the Data Structures and Algorithms in C#. Basic C# programming knowledge would be an added advantage. What You Will Learn ? How to use arrays and lists to get better results in complex scenarios ? Implement algorithms like the Tower of Hanoi on stacks of C# objects ? Build enhanced applications by using hashtables, dictionaries and sets ? Make a positive impact on efficiency of applications with tree traversal ? Effectively find the shortest path in the graph In Detail Data structures allow organizing data efficiently. They are critical to various problems and their suitable implementation can provide a complete solution that acts like reusable code. In this book, you will learn how to use various data structures while developing in the C# language as well as how to implement some of the most common algorithms used with such data structures. At the beginning, you will get to know arrays, lists, dictionaries, and sets together with real-world examples of your application. Then, you will learn how to create and use stacks and queues. In the following part of the book, the more complex data structures will be introduced, namely trees and graphs, together with some algorithms for searching the shortest path in a graph. We will also discuss how to organize the code in a manageable, consistent, and extendable way. By the end of the book,you will learn how to build components that are easy to understand, debug, and use in different applications. Style and approach Readers will be taken through all the indispensable data structures and algorithms so they can begin their coding journey in C#. At each step, the book will show how to implement these via examples while also discussing the attributes of each algorithm so readers are capable to make an informed choice.

Leverage the power of the Reinforcement Learning techniques to develop self-learning systems using Tensorflow About This Book ? Learn reinforcement learning concepts and their implementation using TensorFlow ? Discover different problem-solving methods for Reinforcement Learning ? Apply reinforcement learning for autonomous driving cars, robobrokers, and more Who This Book Is For If you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience with Reinforcement Learning is required. What You Will Learn ? Implement state-of-the-art Reinforcement Learning algorithms from the basics ? Discover various techniques of Reinforcement Learning such as MDP, Q Learning and more ? Learn the applications of Reinforcement Learning in advertisement, image processing, and NLP ? Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym ? Understand how Reinforcement Learning Applications are used in robotics In Detail Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym. Style and approach An Easy-to-follow, step-by-step guide to help you get to grips with real-world applications of Reinforcement Learning with TensorFlow.

A complete guide to designing and building fun games with Qt and Qt Quick using associated toolsets About This Book ? A step by step guide to learn Qt by building simple yet entertaining games ? Get acquainted with a small yet powerful addition—Qt Gamepad Module, that enables Qt applications to support the use of gamepad hardware ? Understand technologies such as QML, OpenGL, and Qt Creator to design intuitive games Who This Book Is For If you want to create great graphical user interfaces and astonishing games with Qt, this book is ideal for you. No previous knowledge of Qt is required; however knowledge of C++ is mandatory. What You Will Learn ? Install the latest version of Qt on your system ? Understand the basic concepts of every Qt game and application ? Develop 2D object-oriented graphics using Qt Graphics View ? Build multiplayer games or add a chat function to your games with Qt Network module ? Script your game with Qt QML ? Explore the Qt Gamepad module in order to integrate gamepad support in C++ and QML applications ? Program resolution-independent and fluid UIs using QML and Qt Quick ? Control your game flow in line with mobile device sensors ? Test and debug your game easily with Qt Creator and Qt Test In Detail Qt is the leading cross-platform toolkit for all significant desktop, mobile, and embedded platforms and is becoming popular by the day, especially on mobile and embedded devices. It's a powerful tool that perfectly fits the needs of game developers. This book will help you learn the basics of Qt and will equip you with the necessary toolsets to build apps and games. The book begins by how to create an application and prepare a working environment for both desktop and mobile platforms. You will learn how to use built-in Qt widgets and Form Editor to create a GUI application and then learn the basics of creating graphical interfaces and Qt's core concepts. Further, you'll learn to enrich your games by implementing network connectivity and employing scripting. You will learn about Qt's capabilities for handling strings and files, data storage, and serialization. Moving on, you will learn about the new Qt Gamepad module and how to add it in your game and then delve into OpenGL and Vulcan, and how it can be used in Qt applications to implement hardware-accelerated 2D and 3D graphics. You will then explore various facets of Qt Quick: how it can be used in games to add game logic, add game physics, and build astonishing UIs for your games. By the end of this book, you will have developed the skillset to develop interesting games with Qt. Style and approach Learn Qt with the help of numerous sample games, introduced step-by-step in each chapter

Learn how to develop web applications that deploy cross-platform and are optimized for high performance using ASP.NET Core 2 About This Book ? Master high-level web app performance improvement techniques using ASP.NET Core 2.0 ? Find the right balance between premature optimization and inefficient code ? Design workflows that run asynchronously and are resilient to transient performance issues Who This Book Is For This book is aimed for readers who can build a web application and have some experience with ASP.NET or some other web application framework (such as Ruby on Rails or Django). They can be people who are happy learning details independently but who struggle to discover the topics that they should be researching. The reader should be interested in improving the performance of their web app and in learning about ASP.NET Core and modern C#. What You Will Learn ? Understand ASP.NET Core 2 and how it differs from its predecessor ? Address performance issues at the early stages of development ? Set up development environments on Windows, Mac, and Linux ? Measure, profile and find the most significant problems ? Identify the differences between development workstations and production infrastructures, and how these can exacerbate problems ? Boost the performance of your application but with an eye to how it affects complexity and maintenance ? Explore a few cutting-edge techniques such as advanced hashing and custom transports In Detail The ASP.NET Core 2 framework is used to develop high-performance and cross-platform web applications. It is built on .NET Core 2 and includes significantly more framework APIs than version 1. This book addresses high-level performance improvement techniques. It starts by showing you how to locate and measure problems and then shows you how to solve some of the most common ones. Next, it shows you how to get started with ASP.NET Core 2 on Windows, Mac, Linux, and with Docker containers. The book illustrates what problems can occur as latency increases when deploying to a cloud infrastructure. It also shows you how to optimize C# code and choose the best data structures for the job. It covers new features in C# 6 and 7, along with parallel programming and distributed architectures. By the end of this book, you will be fixing latency issues and optimizing performance problems, but you will also know how this affects the complexity and maintenance of your application. Finally, we will explore a few highly advanced techniques for further optimization. Style and approach A step-by-step practical guide filled with real-world use cases and examples

Learn to implement DevOps using Docker & Kubernetes. About This Book ? Learning DevOps, container, and Kubernetes within one book. ? Leverage Kubernetes as a platform to deploy, scale, and run containers efficiently. ? A practical guide towards container management and orchestration Who This Book Is For This book is targeted for anyone, who wants to learn containerization and clustering in a practical way using Kubernetes. No prerequisite skills required, however, essential DevOps skill and public/private Cloud knowledge will accelerate the reading speed. If you’re advanced readers, you can also get a deeper understanding of all the tools and technique described in the book. What You Will Learn ? Learn fundamental and advanced DevOps skills and tools ? Get a comprehensive understanding for container ? Learn how to move your application to container world ? Learn how to manipulate your application by Kubernetes ? Learn how to work with Kubernetes in popular public cloud ? Improve time to market with Kubernetes and Continuous Delivery ? Learn how to monitor, log, and troubleshoot your application with Kubernetes In Detail Containerization is said to be the best way to implement DevOps. Google developed Kubernetes, which orchestrates containers efficiently and is considered the frontrunner in container orchestration. Kubernetes is an orchestrator that creates and manages your containers on clusters of servers. This book will guide you from simply deploying a container to administrate a Kubernetes cluster, and then you will learn how to do monitoring, logging, and continuous deployment in DevOps. The initial stages of the book will introduce the fundamental DevOps and the concept of containers. It will move on to how to containerize applications and deploy them into. The book will then introduce networks in Kubernetes. We then move on to advanced DevOps skills such as monitoring, logging, and continuous deployment in Kubernetes. It will proceed to introduce permission control for Kubernetes resources via attribute-based access control and role-based access control. The final stage of the book will cover deploying and managing your container clusters on the popular public cloud Amazon Web Services and Google Cloud Platform. At the end of the book, other orchestration frameworks, such as Docker Swarm mode, Amazon ECS, and Apache Mesos will be discussed. Style and approach Readers will be taken through fundamental DevOps skills and Kubernetes concept and administration with detailed examples. It introduces comprehensive DevOps topics, including microservices, automation tools, containers, monitoring, logging, continuous delivery, and popular public cloud environments. At each step readers will learn how to leverage Kubernetes in their everyday lives and transform their original delivery pipeline for fast and efficient delivery.

Learn how to confidently install, configure, secure, and fully utilize your ArcGIS Enterprise system. About This Book ? Install and configure the components of ArcGIS Enterprise to meet your organization's requirements ? Administer all aspects of ArcGIS Enterprise through user interfaces and APIs ? Optimize and Secure ArcGIS Enterprise to make it run efficiently and effectively Who This Book Is For This book will be geared toward senior GIS analysts, GIS managers, GIS administrators, DBAs, GIS architects, and GIS engineers that need to install, configure, and administer ArcGIS Enterprise 10.5.1. What You Will Learn ? Effectively install and configure ArcGIS Enterprise, including the Enterprise geodatabase, ArcGIS Server, and Portal for ArcGIS ? Incorporate different methodologies to manage and publish services ? Utilize the security methods available in ArcGIS Enterprise ? Use Python and Python libraries from Esri to automate administrative tasks ? Identify the common pitfalls and errors to get your system back up and running quickly from an outage In Detail ArcGIS Enterprise, the next evolution of the ArcGIS Server product line, is a full-featured mapping and analytics platform. It includes a powerful GIS web services server and a dedicated Web GIS infrastructure for organizing and sharing your work. You will learn how to first install ArcGIS Enterprise to then plan, design, and finally publish and consume GIS services. You will install and configure an Enterprise geodatabase and learn how to administer ArcGIS Server, Portal, and Data Store through user interfaces, the REST API, and Python *s. This book starts off by explaining how ArcGIS Enterprise 10.5.1 is different from earlier versions of ArcGIS Server and covers the installation of all the components required for ArcGIS Enterprise. We then move on to geodatabase administration and content publication, where you will learn how to use ArcGIS Server Manager to view the server logs, stop and start services, publish services, define users and roles for security, and perform other administrative tasks. You will also learn how to apply security mechanisms on ArcGIS Enterprise and safely expose services to the public in a secure manner. Finally, you’ll use the RESTful administrator API to automate server management tasks using the Python *ing language. You’ll learn all the best practices and troubleshooting methods to streamline the management of all the interconnected parts of ArcGIS Enterprise. Style and approach The book takes a pragmatic approach, starting with installation & configuration of ArcGIS Enterprise to finally building a robust GIS web infrastructure for your organization.

Learn the basics of Geographic Information Systems by solving real-world problems with powerful open source tools About This Book ? This easy-to-follow guide allows you to manage and analyze geographic data with ease using open source tools ? Publish your geographical data online ? Learn the basics of geoinformatics in a practical way by solving problems Who This Book Is For The book is for IT professionals who have little or no knowledge of GIS. It’s also useful for those who are new to the GIS field who don’t want to spend a lot of money buying licenses of commercial tools and training. What You Will Learn ? Collect GIS data for your needs ? Store the data in a PostGIS database ? Exploit the data using the power of the GIS queries ? Analyze the data with basic and more advanced GIS tools ? Publish your data and share it with others ? Build a web map with your published data In Detail The most commonly used GIS tools automate tasks that were historically done manually—compiling new maps by overlaying one on top of the other or physically cutting maps into pieces representing specific study areas, changing their projection, and getting meaningful results from the various layers by applying mathematical functions and operations. This book is an easy-to-follow guide to use the most matured open source GIS tools for these tasks. We’ll start by setting up the environment for the tools we use in the book. Then you will learn how to work with QGIS in order to generate useful spatial data. You will get to know the basics of queries, data management, and geoprocessing. After that, you will start to practice your knowledge on real-world examples. We will solve various types of geospatial analyses with various methods. We will start with basic GIS problems by imitating the work of an enthusiastic real estate agent, and continue with more advanced, but typical tasks by solving a decision problem. Finally, you will find out how to publish your data (and results) on the web. We will publish our data with QGIS Server and GeoServer, and create a basic web map with the API of the lightweight Leaflet web mapping library. Style and approach The book guides you step by step through each of the core concepts of the GIS toolkit, building an overall picture of its capabilities. This guide approaches the topic systematically, allowing you to build upon what you learned in previous chapters. By the end of this book, you’ll have an understanding of the aspects of building a GIS system and will be able to take that knowledge with you to whatever project calls for it.

Become an expert in implementing advanced, network-related tasks with Python. About This Book ? Build the skills to perform all networking tasks using Python with ease ? Use Python for network device automation, DevOps, and software-defined networking ? Get practical guidance to networking with Python Who This Book Is For If you are a network engineer or a programmer who wants to use Python for networking, then this book is for you. A basic familiarity with networking-related concepts such as TCP/IP and a familiarity with Python programming will be useful. What You Will Learn ? Review all the fundamentals of Python and the TCP/IP suite ? Use Python to execute commands when the device does not support the API or programmatic interaction with the device ? Implement automation techniques by integrating Python with Cisco, Juniper, and Arista eAPI ? Integrate Ansible using Python to control Cisco, Juniper, and Arista networks ? Achieve network security with Python ? Build Flask-based web-service APIs with Python ? Construct a Python-based migration plan from a legacy to scalable SDN-based network. In Detail This book begins with a review of the TCP/ IP protocol suite and a refresher of the core elements of the Python language. Next, you will start using Python and supported libraries to automate network tasks from the current major network vendors. We will look at automating traditional network devices based on the command-line interface, as well as newer devices with API support, with hands-on labs. We will then learn the concepts and practical use cases of the Ansible framework in order to achieve your network goals. We will then move on to using Python for DevOps, starting with using open source tools to test, secure, and analyze your network. Then, we will focus on network monitoring and visualization. We will learn how to retrieve network information using a polling mechanism, ?ow-based monitoring, and visualizing the data programmatically. Next, we will learn how to use the Python framework to build your own customized network web services. In the last module, you will use Python for SDN, where you will use a Python-based controller with OpenFlow in a hands-on lab to learn its concepts and applications. We will compare and contrast OpenFlow, OpenStack, OpenDaylight, and NFV. Finally, you will use everything you’ve learned in the book to construct a migration plan to go from a legacy to a scalable SDN-based network. Style and approach An easy-to-follow guide packed with hands-on examples of using Python for network device automation, DevOps, and SDN.