Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more. Key Features * Use R 3.5 to implement real-world examples in machine learning * Implement key machine learning algorithms to understand the working mechanism of smart models * Create end-to-end machine learning pipelines using modern libraries from the R ecosystem Book Description Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R. What you will learn * Introduce yourself to the basics of machine learning with R 3.5 * Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your results * Learn to build predictive models with the help of various machine learning techniques * Use R to visualize data spread across multiple dimensions and extract useful features * Use interactive data analysis with R to get insights into data * Implement supervised and unsupervised learning, and NLP using R libraries Who this book is for This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.

Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python. Key Features * Get a hands-on, fast-paced introduction to the Python data science stack * Explore ways to create useful metrics and statistics from large datasets * Create detailed analysis reports with real-world data Book Description Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems. The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools. By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs. What you will learn * Use Python to read and transform data into different formats * Generate basic statistics and metrics using data on disk * Work with computing tasks distributed over a cluster * Convert data from various sources into storage or querying formats * Prepare data for statistical analysis, visualization, and machine learning * Present data in the form of effective visuals Who this book is for Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.

Apply cloud native patterns and practices to deliver responsive, resilient, elastic, and message-driven systems with confidence Key Features * Discover best practices for applying cloud native patterns to your cloud applications * Explore ways to effectively plan resources and technology stacks for high security and fault tolerance * Gain insight into core architectural principles using real-world examples Book Description Cloud computing has proven to be the most revolutionary IT development since virtualization. Cloud native architectures give you the benefit of more flexibility over legacy systems. This Learning Path teaches you everything you need to know for designing industry-grade cloud applications and efficiently migrating your business to the cloud. It begins by exploring the basic patterns that turn your database inside out to achieve massive scalability. You’ll learn how to develop cloud native architectures using microservices and serverless computing as your design principles. Then, you’ll explore ways to continuously deliver production code by implementing continuous observability in production. In the concluding chapters, you’ll learn about various public cloud architectures ranging from AWS and Azure to the Google Cloud Platform, and understand the future trends and expectations of cloud providers. By the end of this Learning Path, you’ll have learned the techniques to adopt cloud native architectures that meet your business requirements. This Learning Path includes content from the following Packt products: * Cloud Native Development Patterns and Best Practices by John Gilbert * Cloud Native Architectures by Erik Farr et al. What you will learn * Understand the difference between cloud native and traditional architecture * Automate security controls and configuration management * Minimize risk by evolving your monolithic systems into cloud native applications * Explore the aspects of migration, when and why to use it * Apply modern delivery and testing methods to continuously deliver production code * Enable massive scaling by turning your database inside out Who this book is for This Learning Path is designed for developers who want to progress into building cloud native systems and are keen to learn the patterns involved. Software architects, who are keen on designing scalable and highly available cloud native applications, will also find this Learning Path very useful. To easily grasp these concepts, you will need basic knowledge of programming and cloud computing.

Explore Qt framework and APIs for building cross-platform applications for mobile devices, embedded systems, and IoT Key Features * Build cross-platform applications and deploy them across mobile and connected devices * Design 2D and 3D UIs for embedded systems using Yocto and Qt Creator * Build machine to machine automation solution using QtSensors, QtMQTT, and QtWebSockets Book Description Qt is a world-class framework, helping you to develop rich graphical user interfaces (GUIs) and multi-platform applications that run on all major desktop platforms and most mobile or embedded platforms. The framework helps you connect the dots across platforms and between online and physical experience. This book will help you leverage the fully-featured Qt framework and its modular cross-platform library classes and intuitive APIs to develop applications for mobile, IoT, and industrial embedded systems. Considerations such as screen size, device orientation changes, and small memory will be discussed. We will focus on various core aspects of embedded and mobile systems, such as connectivity, networking, and sensors; there is no IoT without sensors. You will learn how to quickly design a flexible, fast, and responsive UI that looks great. Going further, you will implement different elements in a matter of minutes and synchronize the UI elements with the 3D assets with high precision. You will learn how to create high-performance embedded systems with 3D/2D user interfaces, and deploy and test on your target hardware. The book will explore several new features, including Qt for WebAssembly. At the end of this book, you will learn about creating a full software stack for embedded Linux systems using Yocto and Boot to Qt for Device Creation. What you will learn * Explore the latest features of Qt, such as preview for Qt for Python and Qt for WebAssembly * Create fluid UIs with a dynamic layout for different sized screens * Deploy embedded applications on Linux systems using Yocto * Design Qt APIs for building applications for embedded and mobile devices * Utilize connectivity for networked and machine automated applications * Discover effective techniques to apply graphical effects using Qt Quick apps Who this book is for The book is ideal for mobile developers, embedded systems engineers and enthusiasts who are interested in building cross-platform applications with Qt. Prior knowledge of C++ is required.

Create end-to-end systems that can power robots with artificial vision and deep learning techniques Key Features * Study ROS, the main development framework for robotics, in detail * Learn all about convolutional neural networks, recurrent neural networks, and robotics * Create a chatbot to interact with the robot Book Description Artificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You'll compare different methods used to work with robots and explore computer vision, its algorithms, and limits. You'll then learn how to control the robot with natural language processing commands. You'll study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You'll create a simple Word2Vec model with Keras, as well as build a convolutional neural network (CNN) and improve it with data augmentation and transfer learning. You'll study the ROS and build a conversational agent to manage your robot. You'll also integrate your agent with the ROS and convert an image to text and text to speech. You'll learn to build an object recognition system using a video. By the end of this book, you'll have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment. What you will learn * Explore the ROS and build a basic robotic system * Understand the architecture of neural networks * Identify conversation intents with NLP techniques * Learn and use the embedding with Word2Vec and GloVe * Build a basic CNN and improve it using generative models * Use deep learning to implement artificial intelligence(AI)and object recognition * Develop a simple object recognition system using CNNs * Integrate AI with ROS to enable your robot to recognize objects Who this book is for Artificial Vision and Language Processing for Robotics is for robotics engineers who want to learn how to integrate computer vision and deep learning techniques to create complete robotic systems. It will prove beneficial to you if you have working knowledge of Python and a background in deep learning. Knowledge of the ROS is a plus.

Explore practical use cases to learn everything from Linux components, and functionalities, through to hardware and software support Key Features * Gain a clear understanding of how to design a Linux environment * Learn more about the architecture of the modern Linux operating system(OS) * Understand infrastructure needs and design a high-performing computing environment Book Description It is very important to understand the flexibility of an infrastructure when designing an efficient environment. In this book, you will cover everything from Linux components and functionalities through to hardware and software support, which will help you to implement and tune effective Linux-based solutions. This book gets started with an overview of Linux design methodology. Next, you will focus on the core concepts of designing a solution. As you progress, you will gain insights into the kinds of decisions you need to make when deploying a high-performance solution using Gluster File System (GlusterFS). In the next set of chapters, the book will guide you through the technique of using Kubernetes as an orchestrator for deploying and managing containerized applications. In addition to this, you will learn how to apply and configure Kubernetes for your NGINX application. You’ll then learn how to implement an ELK stack, which is composed of Elasticsearch, Logstash, and Kibana. In the concluding chapters, you will focus on installing and configuring a Saltstack solution to manage different Linux distributions, and explore a variety of design best practices. By the end of this book, you will be well-versed with designing a high-performing computing environment for complex applications to run on. By the end of the book, you will have delved inside the most detailed technical conditions of designing a solution, and you will have also dissected every aspect in detail in order to implement and tune open source Linux-based solutions What you will learn * Study the basics of infrastructure design and the steps involved * Expand your current design portfolio with Linux-based solutions * Discover open source software-based solutions to optimize your architecture * Understand the role of high availability and fault tolerance in a resilient design * Identify the role of containers and how they improve your continuous integration and continuous deployment pipelines * Gain insights into optimizing and making resilient and highly available designs by applying industry best practices Who this book is for This intermediate-level book is for Linux system administrators, Linux support engineers, DevOps engineers, Linux consultants or any open source technology professional looking to learn or expand their knowledge in architecting, designing and implementing solutions based on Linux and open source software. Prior experience in Linux is required.

Master scala's advanced techniques to solve real-world problems in data analysis and gain valuable insights from your data Key Features * A beginner's guide for performing data analysis loaded with numerous rich, practical examples * Access to popular Scala libraries such as Breeze, Saddle for efficient data manipulation and exploratory analysis * Develop applications in Scala for real-time analysis and machine learning in Apache Spark Book Description Efficient business decisions with an accurate sense of business data helps in delivering better performance across products and services. This book helps you to leverage the popular Scala libraries and tools for performing core data analysis tasks with ease. The book begins with a quick overview of the building blocks of a standard data analysis process. You will learn to perform basic tasks like Extraction, Staging, Validation, Cleaning, and Shaping of datasets. You will later deep dive into the data exploration and visualization areas of the data analysis life cycle. You will make use of popular Scala libraries like Saddle, Breeze, Vegas, and PredictionIO for processing your datasets. You will learn statistical methods for deriving meaningful insights from data. You will also learn to create applications for Apache Spark 2.x on complex data analysis, in real-time. You will discover traditional machine learning techniques for doing data analysis. Furthermore, you will also be introduced to neural networks and deep learning from a data analysis standpoint. By the end of this book, you will be capable of handling large sets of structured and unstructured data, perform exploratory analysis, and building efficient Scala applications for discovering and delivering insights What you will learn * Techniques to determine the validity and confidence level of data * Apply quartiles and n-tiles to datasets to see how data is distributed into many buckets * Create data pipelines that combine multiple data lifecycle steps * Use built-in features to gain a deeper understanding of the data * Apply Lasso regression analysis method to your data * Compare Apache Spark API with traditional Apache Spark data analysis Who this book is for If you are a data scientist or a data analyst who wants to learn how to perform data analysis using Scala, this book is for you. All you need is knowledge of the basic fundamentals of Scala programming.

Troubleshoot query performance issues, identify anti-patterns in code, and write efficient T-SQL queries Key Features * Discover T-SQL functionalities and services that help you interact with relational databases * Understand the roles, tasks and responsibilities of a T-SQL developer * Explore solutions for carrying out database querying tasks, database administration, and troubleshooting Book Description Transact-SQL (T-SQL) is Microsoft's proprietary extension to the SQL language that is used with Microsoft SQL Server and Azure SQL Database. This book will be a useful guide to learning the art of writing efficient T-SQL code in modern SQL Server versions, as well as the Azure SQL Database. The book will get you started with query processing fundamentals to help you write powerful, performant T-SQL queries. You will then focus on query execution plans and learn how to leverage them for troubleshooting. In the later chapters, you will learn how to identify various T-SQL patterns and anti-patterns. This will help you analyze execution plans to gain insights into current performance, and determine whether or not a query is scalable. You will also learn to build diagnostic queries using dynamic management views (DMVs) and dynamic management functions (DMFs) to address various challenges in T-SQL execution. Next, you will study how to leverage the built-in tools of SQL Server to shorten the time taken to address query performance and scalability issues. In the concluding chapters, the book will guide you through implementing various features, such as Extended Events, Query Store, and Query Tuning Assistant using hands-on examples. By the end of this book, you will have the skills to determine query performance bottlenecks, avoid pitfalls, and discover the anti-patterns in use. Foreword by Conor Cunningham, Partner Architect – SQL Server and Azure SQL – Microsoft What you will learn * Use Query Store to understand and easily change query performance * Recognize and eliminate bottlenecks that lead to slow performance * Deploy quick fixes and long-term solutions to improve query performance * Implement best practices to minimize performance risk using T-SQL * Achieve optimal performance by ensuring careful query and index design * Use the latest performance optimization features in SQL Server 2017 and SQL Server 2019 * Protect query performance during upgrades to newer versions of SQL Server Who this book is for This book is for database administrators, database developers, data analysts, data scientists, and T-SQL practitioners who want to get started with writing T-SQL code and troubleshooting query performance issues, through the help of practical examples. Previous knowledge of T-SQL querying is not required to get started on this book.

A practical guide to administer, monitor and replicate your PostgreSQL 11 database Key Features * Study and apply the newly introduced features in PostgreSQL 11 * Tackle any problem in PostgreSQL 11 administration and management * Catch up on expert techniques for monitoring, fine-tuning, and securing your database Book Description PostgreSQL is a powerful, open source database management system with an enviable reputation for high performance and stability. With many new features in its arsenal, PostgreSQL 11 allows you to scale up your PostgreSQL infrastructure. This book takes a step-by-step, recipe-based approach to effective PostgreSQL administration. The book will introduce you to new features such as logical replication, native table partitioning, additional query parallelism, and much more to help you to understand and control, crash recovery and plan backups. You will learn how to tackle a variety of problems and pain points for any database administrator such as creating tables, managing views, improving performance, and securing your database. As you make steady progress, the book will draw attention to important topics such as monitoring roles, backup, and recovery of your PostgreSQL 11 database to help you understand roles and produce a summary of log files, ensuring high availability, concurrency, and replication. By the end of this book, you will have the necessary knowledge to manage your PostgreSQL 11 database efficiently. What you will learn * Troubleshoot open source PostgreSQL version 11 on various platforms * Deploy best practices for planning and designing live databases * Select and implement robust backup and recovery techniques in PostgreSQL 11 * Use pgAdmin or OmniDB to perform database administrator (DBA) tasks * Adopt efficient replication and high availability techniques in PostgreSQL * Improve the performance of your PostgreSQL solution Who this book is for This book is designed for database administrators, data architects, database developers, or anyone with an interest in planning and running live production databases using PostgreSQL 11. It is also ideal if you’re looking for hands-on solutions to any problem associated with PostgreSQL 11 administration. Some experience with handling PostgreSQL databases will be beneficial

Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key Features * Get to grips with the fundamentals of deep learning and neural networks * Use R 3.5 and its libraries and APIs to build deep learning models for computer vision and text processing * Implement effective deep learning systems in R with the help of end-to-end projects Book Description Deep learning finds practical applications in several domains, while R is the preferred language for designing and deploying deep learning models. This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The book will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R. By the end of this Learning Path, you’ll be well versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects. This Learning Path includes content from the following Packt products: * R Deep Learning Essentials - Second Edition by F. Wiley and Mark Hodnett * R Deep Learning Projects by Yuxi (Hayden) Liu and Pablo Maldonado What you will learn * Implement credit card fraud detection with autoencoders * Train neural networks to perform handwritten digit recognition using MXNet * Reconstruct images using variational autoencoders * Explore the applications of autoencoder neural networks in clustering and dimensionality reduction * Create natural language processing (NLP) models using Keras and TensorFlow in R * Prevent models from overfitting the data to improve generalizability * Build shallow neural network prediction models Who this book is for This Learning Path is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. A fundamental understanding of R programming and familiarity with the basic concepts of deep learning are necessary to get the most out of this Learning Path.

Master machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languages Key Features * Gain expertise in machine learning, deep learning and other techniques * Build intelligent end-to-end projects for finance, social media, and a variety of domains * Implement multi-class classification, regression, and clustering Book Description R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You'll tackle realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. You'll explore different clustering techniques to segment customers using wholesale data and use TensorFlow and Keras-R for performing advanced computations. You’ll also be introduced to reinforcement learning along with its various use cases and models. Additionally, it shows you how some of these black-box models can be diagnosed and understood. By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects. This Learning Path includes content from the following Packt products: * R Machine Learning Projects by Dr. Sunil Kumar Chinnamgari * Mastering Machine Learning with R - Third Edition by Cory Lesmeister What you will learn * Develop a joke recommendation engine to recommend jokes that match users’ tastes * Build autoencoders for credit card fraud detection * Work with image recognition and convolutional neural networks * Make predictions for casino slot machine using reinforcement learning * Implement NLP techniques for sentiment analysis and customer segmentation * Produce simple and effective data visualizations for improved insights * Use NLP to extract insights for text * Implement tree-based classifiers including random forest and boosted tree Who this book is for If you are a data analyst, data scientist, or machine learning developer this is an ideal Learning Path for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this Learning Path.

The cult Swedish graphic novel that inspired the critically acclaimed Lukas Moodysson film We Are the Best! Coco, Klara and Mathilda have known each other since primary school, where they met in folk dancing class. Now they’re almost teenagers, and their anarchist ideals and dreams of forming a world-beating punk band set them apart from the other girls at school. They can’t play any instruments, practice with pillows and pans, and keep getting told that punk is dead. But they’re not going to let any of those things get in their way… Published in English for the first time, Never Goodnight is a hilarious and life-affirming memoir which will remind you that all you need in life is your best friends, a can of hairspray and three guitar chords.

This step-by-step guide will help you gain a deep understanding of the lightweight MQTT protocol. We’ll begin with the specific vocabulary of MQTT and its working modes, followed by installing a Mosquitto MQTT broker. Then, you will use best practices to secure the MQTT Mosquitto broker to ensure that only authorized clients are able to publish and receive messages. Once you have secured the broker with the appropriate configuration, you will develop a solution that controls a drone with Python. Further on, you will use Python on a Raspberry Pi 3 board to process commands and Python on Intel Boards (Joule, Edison and Galileo). You will then connect to the MQTT broker, subscribe to topics, send messages, and receive messages in Python. You will also develop a solution that interacts with sensors in Java by working with MQTT messages. Moving forward, you will work with an asynchronous API with callbacks to make the sensors interact with MQTT messages. Following the same process, you will develop an iOS app with Swift 3, build a website that uses WebSockets to connect to the MQTT broker, and control home automation devices with HTML5, JavaScript code, Node.js and MQTT messages What you will learn ?Understand how MQTTv3.1 and v3.1.1 works in detail ?Install and secure a Mosquitto MQTT broker by following best practices ?Design and develop IoT solutions combined with mobile and web apps that use MQTT messages to communicate ?Explore the features included in MQTT for IoT and Machine-to-Machine communications ?Publish and receive MQTT messages with Python, Java, Swift, JavaScript, and Node.js ?Implement the security best practices while setting up the MQTT Mosquitto broker

Dracula Unbound, Frankenstein Unbound and Moreau’s Other Island all together in one eBook. All of Aliss’ Monster Trilogy in one place. Moreau’s Other Island Welcome to Dr Moreau’s other island. Place of untold horros. Home of the Beast Men… Available for the first time in eBook. He stands very tall, long prosthetic limbs glistening in the harsh sun, withered body swaying, carbine and whip clasped in artificial hands. Man-beasts cower on the sand as he brandishes his gun in the air. He is Dr Moreau, ruler of the fabulous, grotesque island, where humans are as brutes and brutes as humans, where the future of the entire human race is being reprogrammed. The place of untold horrors. The place of the New Man. Frankenstein Unbound When Joe Bodenland is suddenly transported back in time to the year 1816, his first reaction is of eager curiosity rather than distress… This is Aldiss’ response to Mary Shelley’s Frankenstein, available for the first time in eBook. When Joe Bodenland is suddenly transported back in time to the year 1816, his first reaction is of eager curiosity rather than distress. Certainly the Switzerland in which he finds himself, with its charming country inns, breathtaking landscapes and gentle, unmechanised pace of life, is infinitely preferable to the America of 2020 where the games of politicians threaten total annihilation. But after meeting the brooding young Victor Frankenstein, Joe realises that this world is more complex than the one he left behind. Is Frankenstein real, or are both Joe and he living out fictional lives? Dracula Unbound A dramatic reworking of the vampire myth in a way that only Brian Aldiss can… Available for the first time in eBook. When Bram Stoker was writing his famous novel, Dracula, at the end of the 19th century he received a visitor named Joe Bodenland. While the real Count Dracula came from the distant past, Joe arrived from Stoker’s future – on a desperate mission to save humanity from the undead.

Over 70 recipes for system administrators or DevOps to master Kali Linux 2 and perform effective security assessments About This Book ?Set up a penetration testing lab to conduct a preliminary assessment of attack surfaces and run exploits ?Improve your testing efficiency with the use of automated vulnerability scanners ?Work through step-by-step recipes to detect a wide array of vulnerabilities, exploit them to analyze their consequences, and identify security anomalies Who This Book Is For This book is intended for those who want to know more about information security. In particular, it's ideal for system administrators and system architects who want to ensure that the infrastructure and systems they are creating and managing are secure. This book helps both beginners and intermediates by allowing them to use it as a reference book and to gain in-depth knowledge. What You Will Learn ?Understand the importance of security assessments over merely setting up and managing systems/processes ?Familiarize yourself with tools such as OPENVAS to locate system and network vulnerabilities ?Discover multiple solutions to escalate privileges on a compromised machine ?Identify security anomalies in order to make your infrastructure secure and further strengthen it ?Acquire the skills to prevent infrastructure and application vulnerabilities ?Exploit vulnerabilities that require a complex setup with the help of Metasploit In Detail With the increasing threats of breaches and attacks on critical infrastructure, system administrators and architects can use Kali Linux 2.0 to

Amazon Web Services (AWS) is a bundled remote computing service that provides cloud computing infrastructure over the Internet with storage, bandwidth, and customized support for application programming interfaces (API). Implementing these services to efficiently administer your cloud environments is a core task. This book will help you build and administer your cloud environment with AWS. We'll begin with the AWS fundamentals, and you'll build the foundation for the recipes you'll work on throughout the book. Next, you will find out how to manage multiple accounts and set up consolidated billing. You will then learn to set up reliable and fast hosting for static websites, share data between running instances, and back up your data for compliance. Moving on, you will find out how to use the compute service to enable consistent and fast instance provisioning, and will see how to provision storage volumes and autoscale an application server. Next, you'll discover how to effectively use the networking and database service of AWS. You will also learn about the different management tools of AWS along with securing your AWS cloud. Finally, you will learn to estimate the costs for your cloud. By the end of the book, you will be able to easily administer your AWS cloud. What you will learn ?Discover the best practices to achieve an automated repeatable infrastructure in AWS ?Bring down your IT costs by managing AWS successfully and deliver high availability, fault tolerance, and scalability ?Make any website faster with static and dynamic caching ?Create monitoring and alerting dashboards using CloudWatch ?Migrate a database to AWS ?Set up consolidated billing to achieve simple and effective cost management with accounts ?Host a domain and find out how you can automate health checks About the Author Lucas Chan has been working in tech since 1995 in a variety of development, systems admin, and DevOps roles. He is currently a senior consultant and engineer at Versent and technical director at Stax. He's been running production workloads on AWS for over 10 years. He's also a member of the APAC AWS warriors program and holds all five of the available AWS certifications. Rowan Udell has been working in development and operations for 15 years. He has held a variety of positions, such as SRE, frontend developer, backend developer, consultant, technical lead, and team leader. His travels have seen him work in start-ups and enterprises in the finance, education, and web industries in Australia and Canada. He currently works as a senior consultant with Versent, an AWS Advanced Partner in Sydney. He specializes in serverless applications and architectures on AWS, and contributes actively in the Serverless Framework community.

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

Cassandra is a distributed database that stands out thanks to its robust feature set and intuitive interface, while providing high availability and scalability of a distributed data store. This book will introduce you to the rich feature set offered by Cassandra, and empower you to create and manage a highly scalable, performant and fault-tolerant database layer. The book starts by explaining the new features implemented in Cassandra 3.x and get you set up with Cassandra. Then you'll walk through data modeling in Cassandra and the rich feature set available to design a flexible schema. Next you'll learn to create tables with composite partition keys, collections and user-defined types and get to know different methods to avoid denormalization of data. You will then proceed to create user-defined functions and aggregates in Cassandra. Then, you will set up a multi node cluster and see how the dynamics of Cassandra change with it. Finally, you will implement some application-level optimizations using a Java client. By the end of this book, you'll be fully equipped to build powerful, scalable Cassandra database layers for your applications. What you will learn ?Install Cassandra ?Create keyspaces and tables with multiple clustering columns to organize related data ?Use secondary indexes and materialized views to avoid denormalization of data

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.

Utilize proven solutions to solve common problems in game development About This Book ?Untangle your game development workflow, make cleaner code, and create structurally solid games ?Implement key programming patterns that will enable you to make efficient AI and remove duplication ?Optimize your game using memory management techniques Who This Book Is For If you are a game developer who wants to solve commonly-encountered issues or have some way to communicate to other developers in a standardized format, then this book is for you. Knowledge of basic game programming principles and C++ programming is assumed. What You Will Learn ?Learn what design patterns are and why you would want to use them ?Reduce the maintenance burden with well-tested, cleaner code ?Employ the singleton pattern effectively to reduce your compiler workload ?Use the factory pattern to help you create different objects with the same creation logic and reduce coding time ?Improve game performance with Object Pools ?Allow game play to interact with physics or graphics in an abstract way ?Refractor your code to remove common code smells In Detail You've learned how to program, and you've probably created some simple games at some point, but now you want to build larger projects and find out how to resolve your problems. So instead of a coder, you might now want to think like a game developer or software engineer. To organize your code well, you need certain tools to do so, and that's what this book is all about. You will learn techniques to