A comprehensive guide to Android forensics, from setting up the workstation to analyzing key artifacts Key Features *Get up and running with modern mobile forensic strategies and techniques *Analyze the most popular Android applications using free and open source forensic tools *Learn malware detection and analysis techniques to investigate mobile cybersecurity incidents Book Description Many forensic examiners rely on commercial, push-button tools to retrieve and analyze data, even though there is no tool that does either of these jobs perfectly. Learning Android Forensics will introduce you to the most up-to-date Android platform and its architecture, and provide a high-level overview of what Android forensics entails. You will understand how data is stored on Android devices and how to set up a digital forensic examination environment. As you make your way through the chapters, you will work through various physical and logical techniques to extract data from devices in order to obtain forensic evidence. You will also learn how to recover deleted data and forensically analyze application data with the help of various open source and commercial tools. In the concluding chapters, you will explore malware analysis so that you’ll be able to investigate cybersecurity incidents involving Android malware. By the end of this book, you will have a complete understanding of the Android forensic process, you will have explored open source and commercial forensic tools, and will have basic skills of Android malware identification and analysis. What you will learn *Understand Android OS and architecture *Set up a forensics environment for Android analysis *Perform logical and physical data extractions *Learn to recover deleted data *Explore how to analyze application data *Identify malware on Android devices *Analyze Android malware Who this book is for If you are a forensic analyst or an information security professional wanting to develop your knowledge of Android forensics, then this is the book for you. Some basic knowledge of the Android mobile platform is expected.

Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x Key Features *Experimental projects showcasing the implementation of high-performance deep learning models with Keras. * *Use-cases across reinforcement learning, natural language processing, GANs and computer vision. * *Build strong fundamentals of Keras in the area of deep learning and artificial intelligence. Book Description Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems. What you will learn *Apply regression methods to your data and understand how the regression algorithm works *Understand the basic concepts of classification methods and how to implement them in the Keras environment *Import and organize data for neural network classification analysis *Learn about the role of rectified linear units in the Keras network architecture *Implement a recurrent neural network to classify the sentiment of sentences from movie reviews *Set the embedding layer and the tensor sizes of a network Who this book is for If you are a data scientist, machine learning engineer, deep learning practitioner or an AI engineer who wants to build speedy intelligent applications with minimal lines of codes, then this book is the best fit for you. Sound knowledge of machine learning and basic familiarity with Keras library would be useful.

Leverage the power of machine learning on mobiles and build intelligent mobile applications with ease Key Features *Build smart mobile applications for Android and iOS devices *Use popular machine learning toolkits such as Core ML and TensorFlow Lite *Explore cloud services for machine learning that can be used in mobile apps Book Description Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples. You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains. By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices. What you will learn *Build intelligent machine learning models that run on Android and iOS *Use machine learning toolkits such as Core ML, TensorFlow Lite, and more *Learn how to use Google Mobile Vision in your mobile apps *Build a spam message detection system using Linear SVM *Using Core ML to implement a regression model for iOS devices *Build image classification systems using TensorFlow Lite and Core ML Who this book is for If you are a mobile app developer or a machine learning enthusiast keen to use machine learning to build smart mobile applications, this book is for you. Some experience with mobile application development is all you need to get started with this book. Prior experience with machine learning will be an added bonus

Learn the foundations of blockchain technology - its core concepts and algorithmic solutions across cryptography, peer-to-peer technology, and game theory. Key Features * Learn the core concepts and foundations of the blockchain and cryptocurrencies * Understand the protocols and algorithms behind decentralized applications * Master how to architect, build, and optimize blockchain applications Book Description Blockchain technology is a combination of three popular concepts: cryptography, peer-to-peer networking, and game theory. This book is for anyone who wants to dive into blockchain from first principles and learn how decentralized applications and cryptocurrencies really work. This book begins with an overview of blockchain technology, including key definitions, its purposes and characteristics, so you can assess the full potential of blockchain. All essential aspects of cryptography are then presented, as the backbone of blockchain. For readers who want to study the underlying algorithms of blockchain, you’ll see Python implementations throughout. You’ll then learn how blockchain architecture can create decentralized applications. You’ll see how blockchain achieves decentralization through peer-to-peer networking, and how a simple blockchain can be built in a P2P network. You’ll learn how these elements can implement a cryptocurrency such as Bitcoin, and the wider applications of blockchain work through smart contracts. Blockchain optimization techniques, and blockchain security strategies are then presented. To complete this foundation, we consider blockchain applications in the financial and non-financial sectors, and also analyze the future of blockchain. A study of blockchain use cases includes supply chains, payment systems, crowdfunding, and DAOs, which rounds out your foundation in blockchain technology. What you will learn * The core concepts and technical foundations of blockchain * The algorithmic principles and solutions that make up blockchain and cryptocurrencies * Blockchain cryptography explained in detail * How to realize blockchain projects with hands-on Python code * How to architect the blockchain and blockchain applications * Decentralized application development with MultiChain, NEO, and Ethereum * Optimizing and enhancing blockchain performance and security * Classical blockchain use cases and how to implement them Who this book is for This book is for anyone who wants to dive into blockchain technology from first principles and build a foundational knowledge of blockchain. Familiarity with Python will be helpful if you want to follow how the blockchain protocols are implemented. For readers who are blockchain application developers, most of the applications used in this book can be executed on any platform.

Get to grips with the unified, highly scalable distributed storage system and learn how to design and implement it. Key Features * Explore Ceph's architecture in detail * Implement a Ceph cluster successfully and gain deep insights into its best practices * Leverage the advanced features of Ceph, including erasure coding, tiering, and BlueStore Book Description This Learning Path takes you through the basics of Ceph all the way to gaining in-depth understanding of its advanced features. You’ll gather skills to plan, deploy, and manage your Ceph cluster. After an introduction to the Ceph architecture and its core projects, you’ll be able to set up a Ceph cluster and learn how to monitor its health, improve its performance, and troubleshoot any issues. By following the step-by-step approach of this Learning Path, you’ll learn how Ceph integrates with OpenStack, Glance, Manila, Swift, and Cinder. With knowledge of federated architecture and CephFS, you’ll use Calamari and VSM to monitor the Ceph environment. In the upcoming chapters, you’ll study the key areas of Ceph, including BlueStore, erasure coding, and cache tiering. More specifically, you’ll discover what they can do for your storage system. In the concluding chapters, you will develop applications that use Librados and distributed computations with shared object classes, and see how Ceph and its supporting infrastructure can be optimized. By the end of this Learning Path, you'll have the practical knowledge of operating Ceph in a production environment. This Learning Path includes content from the following Packt products: * Ceph Cookbook by Michael Hackett, Vikhyat Umrao and Karan Singh * Mastering Ceph by Nick Fisk * Learning Ceph, Second Edition by Anthony D'Atri, Vaibhav Bhembre and Karan Singh What you will learn * Understand the benefits of using Ceph as a storage solution * Combine Ceph with OpenStack, Cinder, Glance, and Nova components * Set up a test cluster with Ansible and virtual machine with VirtualBox * Develop solutions with Librados and shared object classes * Configure BlueStore and see its interaction with other configurations * Tune, monitor, and recover storage systems effectively * Build an erasure-coded pool by selecting intelligent parameters Who this book is for If you are a developer, system administrator, storage professional, or cloud engineer who wants to understand how to deploy a Ceph cluster, this Learning Path is ideal for you. It will help you discover ways in which Ceph features can solve your data storage problems. Basic knowledge of storage systems and GNU/Linux will be beneficial.

Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications Key Features * Build independent machine learning (ML) systems leveraging the best features of R 3.5 * Understand and apply different machine learning techniques using real-world examples * Use methods such as multi-class classification, regression, and clustering Book Description Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you’ll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you’ll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work. What you will learn * Prepare data for machine learning methods with ease * Understand how to write production-ready code and package it for use * Produce simple and effective data visualizations for improved insights * Master advanced methods, such as Boosted Trees and deep neural networks * Use natural language processing to extract insights in relation to text * Implement tree-based classifiers, including Random Forest and Boosted Tree Who this book is for This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement advanced machine learning algorithms. The book will help you take your skills to the next level and advance further in this field. Working knowledge of machine learning with R is mandatory.

Build, secure, and deploy real-world serverless applications in AWS and peek into the serverless cloud offerings from Azure, Google Cloud, and IBM Cloud Key Features * Build serverless applications with AWS Lambda, AWS CloudFormation and AWS CloudWatch * Perform data analytics and natural language processing(NLP)on the AWS serverless platform * Explore various design patterns and best practices involved in serverless computing Book Description Managing physical servers will be a thing of the past once you’re able to harness the power of serverless computing. If you’re already prepped with the basics of serverless computing, Serverless Programming Cookbook will help you take the next step ahead. This recipe-based guide provides solutions to problems you might face while building serverless applications. You'll begin by setting up Amazon Web Services (AWS), the primary cloud provider used for most recipes. The next set of recipes will cover various components to build a Serverless application including REST APIs, database, user management, authentication, web hosting, domain registration, DNS management, CDN, messaging, notifications and monitoring. The book also introduces you to the latest technology trends such as Data Streams, Machine Learning and NLP. You will also see patterns and practices for using various services in a real world application. Finally, to broaden your understanding of Serverless computing, you'll also cover getting started guides for other cloud providers such as Azure, Google Cloud Platform and IBM cloud. By the end of this book, you’ll have acquired the skills you need to build serverless applications efficiently using various cloud offerings. What you will learn * Serverless computing in AWS and explore services with other clouds * Develop full-stack apps with API Gateway, Cognito, Lambda and DynamoDB * Web hosting with S3, CloudFront, Route 53 and AWS Certificate Manager * SQS and SNS for effective communication between microservices * Monitoring and troubleshooting with CloudWatch logs and metrics * Explore Kinesis Streams, Amazon ML models and Alexa Skills Kit Who this book is for For developers looking for practical solutions to common problems while building a serverless application, this book provides helpful recipes. To get started with this intermediate-level book, knowledge of basic programming is a must.

Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more Key Features *Master machine learning, deep learning, and predictive modeling concepts in R 3.5 *Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains *Implement smart cognitive models with helpful tips and best practices Book Description R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations. What you will learn *Explore deep neural networks and various frameworks that can be used in R *Develop a joke recommendation engine to recommend jokes that match users’ tastes *Create powerful ML models with ensembles to predict employee attrition *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 Who this book is for If you’re a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book 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 book.

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features *Build a strong foundation in neural networks and deep learning with Python libraries *Explore advanced deep learning techniques and their applications across computer vision and NLP *Learn how a computer can navigate in complex environments with reinforcement learning Book Description With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications. What you will learn *Grasp the mathematical theory behind neural networks and deep learning processes *Investigate and resolve computer vision challenges using convolutional networks and capsule networks *Solve generative tasks using variational autoencoders and Generative Adversarial Networks *Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models *Explore reinforcement learning and understand how agents behave in a complex environment *Get up to date with applications of deep learning in autonomous vehicles Who this book is for This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

A practical guide that helps you progress to using modern integration methods and leverage new cloud capability models Key Features * Design critical hybrid integration solutions for your organization * Gain in-depth knowledge of how to build cloud-native integration solutions * Leverage cognitive services to build smart cloud solutions Book Description With more enterprises adapting cloud-based and API-based solutions, application integration has become more relevant and significant than ever before. Parallelly, Serverless Integration has gained popularity, as it helps agile organizations to build integration solutions quickly without having to worry about infrastructure costs. With Microsoft Azure’s serverless offerings, such as Logic Apps, Azure Functions, API Management, Azure Event Grid and Service Bus, organizations can build powerful, secure, and scalable integration solutions with ease. The primary objective of this book is to help you to understand various serverless offerings included within Azure Integration Services, taking you through the basics and industry practices and patterns. This book starts by explaining the concepts of services such as Azure Functions, Logic Apps, and Service Bus with hands-on examples and use cases. After getting to grips with the basics, you will be introduced to API Management and building B2B solutions using Logic Apps Enterprise Integration Pack. This book will help readers to understand building hybrid integration solutions and touches upon Microsoft Cognitive Services and leveraging them in modern integration solutions. Industry practices and patterns are brought to light at appropriate opportunities while explaining various concepts. What you will learn * Learn about the design principles of Microsoft Azure Serverless Integration * Get insights into Azure Functions, Logic Apps, Azure Event Grid and Service Bus * Secure and manage your integration endpoints using Azure API Management * Build advanced B2B solutions using Logic Apps, Enterprise Integration Pack * Monitor integration solutions using tools available on the market * Discover design patterns for hybrid integration Who this book is for Serverless Integration Design Patterns with Azure is for you if you are a solution architect or integration professional aiming to build complex cloud solutions for your organization. Developers looking to build next-level hybrid or cloud solutions will also find this book useful. Prior programming knowledge is necessary.

Designing and deploying solutions using the SAP BusinessObjects Business Intelligence platform 4.2. Key Features * Get up and running with the SAP BusinessObjects Business Intelligence platform * Perform effective data analysis and visualization for actionable insights * Enhance your BI strategy by creating different types of reports and dashboards using SAP BusinessObjects Book Description The SAP BusinessObjects Business Intelligence platform is a powerful reporting and analysis tool. This book is the ideal introduction to the SAP BusinessObjects Business Intelligence platform, introducing you to its data visualization, visual analytics, reporting, and dashboarding capabilities. The book starts with an overview of the BI platform and various data sources for reporting. Then, we move on to looking at data visualization, analysis, reporting, and analytics using BusinessObjects Business Intelligence tools. You will learn about the features associated with reporting, scheduling, and distribution and learn how to deploy the platform. Toward the end, you will learn about the strategies and factors that should be considered during deployment. By the end, you will be confident working with the SAP BusinessObjects Business Intelligence platform to deliver better insights for more effective decision making. What you will learn * Work with various tools to create interactive data visualization and analysis * Query, report, and analyze with SAP Business Objects Web Intelligence * Create a report in SAP Crystal Reports for Enterprise * Visualize and manipulate data using an SAP Lumira Storyboard * Deep dive into the workings of the SAP predictive analytics tool * Deploy and configure SAP BO Intelligence platform 4.2 Who this book is for This book is for Business Intelligence professionals and existing SAP ecosystem users who want to perform effective Business Intelligence using SAP BusinessObjects.

Convert Android to a powerful pentesting platform. Key Features * Get up and running with Kali Linux NetHunter * Connect your Android device and gain full control over Windows, OSX, or Linux devices * Crack Wi-Fi passwords and gain access to devices connected over the same network collecting intellectual data Book Description Kali NetHunter is a version of the popular and powerful Kali Linux pentesting platform, designed to be installed on mobile devices. Hands-On Penetration Testing with Kali NetHunter will teach you the components of NetHunter and how to install the software. You’ll also learn about the different tools included and how to optimize and use a package, obtain desired results, perform tests, and make your environment more secure. Starting with an introduction to Kali NetHunter, you will delve into different phases of the pentesting process. This book will show you how to build your penetration testing environment and set up your lab. You will gain insight into gathering intellectual data, exploiting vulnerable areas, and gaining control over target systems. As you progress through the book, you will explore the NetHunter tools available for exploiting wired and wireless devices. You will work through new ways to deploy existing tools designed to reduce the chances of detection. In the concluding chapters, you will discover tips and best practices for integrating security hardening into your Android ecosystem. By the end of this book, you will have learned to successfully use a mobile penetration testing device based on Kali NetHunter and Android to accomplish the same tasks you would traditionally, but in a smaller and more mobile form factor. What you will learn * Choose and configure a hardware device to use Kali NetHunter * Use various tools during pentests * Understand NetHunter suite components * Discover tips to effectively use a compact mobile platform * Create your own Kali NetHunter-enabled device and configure it for optimal results * Learn to scan and gather information from a target * Explore hardware adapters for testing and auditing wireless networks and Bluetooth devices Who this book is for Hands-On Penetration Testing with Kali NetHunter is for pentesters, ethical hackers, and security professionals who want to learn to use Kali NetHunter for complete mobile penetration testing and are interested in venturing into the mobile domain. Some prior understanding of networking assessment and Kali Linux will be helpful.

Learn how to run new and old applications in Docker containers on Windows - modernizing the architecture, improving security and maximizing efficiency. Key Features * Run .NET Framework and .NET Core apps in Docker containers for efficiency, security and portability * Design distributed containerized apps, using enterprise-grade open source software from Docker Hub * Build a CI/CD pipeline with Docker, going from source to a production Docker Swarm in the cloud Book Description Docker on Windows, Second Edition teaches you all you need to know about Docker on Windows, from the 101 to running highly-available workloads in production. You’ll be guided through a Docker journey, starting with the key concepts and simple examples of .NET Framework and .NET Core apps in Docker containers on Windows. Then you’ll learn how to use Docker to modernize the architecture and development of traditional ASP.NET and SQL Server apps. The examples show you how to break up legacy monolithic applications into distributed apps and deploy them to a clustered environment in the cloud, using the exact same artifacts you use to run them locally. You’ll see how to build a CI/CD pipeline which uses Docker to compile, package, test and deploy your applications. To help you move confidently to production, you’ll learn about Docker security, and the management and support options. The book finishes with guidance on getting started with Docker in your own projects. You’ll walk through some real-world case studies for Docker implementations, from small-scale on-premises apps to very large-scale apps running on Azure. What you will learn * Understand key Docker concepts: images, containers, registries and swarms * Run Docker on Windows 10, Windows Server 2019, and in the cloud * Deploy and monitor distributed solutions across multiple Docker containers * Run containers with high availability and failover with Docker Swarm * Master security in-depth with the Docker platform, making your apps more secure * Build a Continuous Deployment pipeline, running Jenkins and Git in Docker * Debug applications running in Docker containers using Visual Studio * Plan the adoption of Docker in your organization Who this book is for If you want to modernize an old monolithic application without rewriting it, smooth the deployment to production, or move to DevOps or the cloud, then Docker is the enabler for you. This book gives you a solid grounding in Docker so you can confidently approach all of these scenarios.

Understand and solve complex computational problems and write efficient code with Kotlin Key Features * Learn about important data structures such as lists, arrays, queues, and stacks * Design custom algorithms for real-life implementations * Identify suitable tools for different scenarios and deliver immediate results Book Description Data structures and algorithms are more than just theoretical concepts. They help you become familiar with computational methods for solving problems and writing logical code. Equipped with this knowledge, you can write efficient programs that run faster and use less memory. Hands-On Data Structures and Algorithms with Kotlin book starts with the basics of algorithms and data structures, helping you get to grips with the fundamentals and measure complexity. You'll then move on to exploring the basics of functional programming while getting used to thinking recursively. Packed with plenty of examples along the way, this book will help you grasp each concept easily. In addition to this, you'll get a clear understanding of how the data structures in Kotlin's collection framework work internally. By the end of this book, you will be able to apply the theory of data structures and algorithms to work out real-world problems. What you will learn * Understand the basic principles of algorithms and data structures * Explore general-purpose data structures with arrays and linked lists * Get to grips with the basics of stacks, queues, and double-ended queues * Understand functional programming and related data structures * Use performant searching and efficient sorting * Uncover how Kotlin's collection framework functions * Become adept at implementing different types of maps Who this book is for If you're a Kotlin developer who wants to learn the intricacies of implementing data structures and algorithms for scalable application development, this book is for you.

A comprehensive guide for MCSA Exam 70-740, that will help you prepare from day one to earn the valuable Microsoft Certificate Key Features * Leverage practice questions and mock tests to pass this certification with confidence * Learn to Install Windows Servers,implement high availability, and monitor server environments * Gain necessary skills to implement and configure storage and compute features Book Description MCSA: Windows Server 2016 certification is one of the most sought-after certifications for IT professionals, which includes working with Windows Server and performing administrative tasks around it. This book is aimed at the 70-740 certification and is part of Packt's three-book series on MCSA Windows Server 2016 certification, which covers Exam 70-740, Exam 70-741, and Exam 70-742. This book will cover exam objectives for the 70-740 exam, and starting from installing and configuring Windows Server 2016, Windows Server imaging and deployment to configuring and managing disks and volumes, implementing and configuring server storage and implementing Hyper-V. At the end of each chapter you will be provided test questions to revise your learnings which will boost your confidence in preparing for the actual certifications. By the end of this book, you will learn everything needed to pass the, MCSA Exam 70-740: Installation, Storage, and Compute with Windows Server 2016, certification. What you will learn * Install Windows Server 2016 * Upgrade and Migrate servers and workloads * Implement and configure server storage * Install and configure Hyper-V * Configure the virtual machine (VM) settings * Configure Hyper-V storage * Configure Hyper-V networking Who this book is for This book is ideal for system administrators interested in installing and configuring storage and compute features with Windows Sever 2016 and aiming to pass the 70-740 certification. Some experience with Windows Server in an enterprise environment is assumed.

Create dynamic dashboards to bring interactive data visualization to your enterprise using Qlik Sense Key Features * Implement various Qlik Sense features to create interactive dashboards * Analyze data easily and make business decisions faster using Qlik Sense * Perform self-service data analytics and geospatial analytics using an example-based approach Book Description Qlik Sense allows you to explore simple-to-complex data to reveal hidden insights and data relationships to make business-driven decisions. Hands-On Business Intelligence with Qlik Sense begins by helping you get to grips with underlying Qlik concepts and gives you an overview of all Qlik Sense’s features. You will learn advanced modeling techniques and learn how to analyze the data loaded using a variety of visualization objects. You’ll also be trained on how to share apps through Qlik Sense Enterprise and Qlik Sense Cloud and how to perform aggregation with AGGR. As you progress through the chapters, you’ll explore the stories feature to create data-driven presentations and update an existing story. This book will guide you through the GeoAnalytics feature with the geo-mapping object and GeoAnalytics connector. Furthermore, you’ll learn about the self-service analytics features and perform data forecasting using advanced analytics. Lastly, you’ll deploy Qlik Sense apps for mobile and tablet. By the end of this book, you will be well-equipped to run successful business intelligence applications using Qlik Sense's functionality, data modeling techniques, and visualization best practices. What you will learn * Discover how to load, reshape, and model data for analysis * Apply data visualization practices to create stunning dashboards * Make use of Python and R for advanced analytics * Perform geo-analysis to create visualizations using native objects * Learn how to work with AGGR and data stories Who this book is for If you’re a data analyst, BI developer, or interested in business intelligence and want to gain practical experience of working on Qlik Sense, this book is for you. You’ll also find it useful if you want to explore Qlik Sense’s next-generation applications for self-service business intelligence. No prior experience of working with Qlik Sense is required.

Build fast, robust, and maintainable modern full-stack web applications using MongoDB, Express, Angular, and Node.js. Key Features * Build highly scalable, asynchronous, and event-driven APIs * Develop a user authentication system with MEAN * Build a full-fledged application using the MEAN stack Book Description MongoDB, Express, Angular and Node.js Fundamentals is a practical guide to the tried-and-true production-ready MEAN stack, with tips and best practices. The book begins by demystifying the MEAN architecture. You’ll take a look at the features of the JavaScript libraries, technologies, and frameworks that make up a MEAN stack. With this book, you'll not only learn how to develop highly scalable, asynchronous, and event-driven APIs quickly with Express and Node.js, but you'll also be able put your full-stack skills to use by building two full-fledged MEAN applications from scratch. You’ll understand how to build a blogging application using the MEAN stack and get to grips with user authentication using MEAN. As you progress through the chapters, you’ll explore some old and new features of Angular, such as pipes, reactive forms, modules and optimizing apps, animations and unit testing, and much more. By the end of the book, you’ll get ready to take control of the MEAN stack and transform into a full-stack JavaScript developer, developing efficient web applications using Javascript technologies. What you will learn * Understand the MEAN architecture * Create RESTful APIs to complete CRUD tasks * Build a blogging application with basic features * Describe best practices to secure node applications * Implement authentication and authorization * Creating simple animations using Angular * Perform unit testing on Angular applications Who this book is for If you are a beginner or intermediate frontend developer who wants to become full-stack JavaScript developer, this book is ideal for you. You'll need some prior exposure to MongoDB as we skim over its basics before getting straight to work.

Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games Key Features * Apply the power of deep learning to complex reasoning tasks by building a Game AI * Exploit the most recent developments in machine learning and AI for building smart games * Implement deep learning models and neural networks with Python Book Description The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning. What you will learn * Learn the foundations of neural networks and deep learning. * Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots. * Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems. * Working with Unity ML-Agents toolkit and how to install, setup and run the kit. * Understand core concepts of DRL and the differences between discrete and continuous action environments. * Use several advanced forms of learning in various scenarios from developing agents to testing games. Who this book is for This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.

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.

Leverage the power of reward-based training for your deep learning models with Python Key Features * Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP) * Study practical deep reinforcement learning using Q-Networks * Explore state-based unsupervised learning for machine learning models Book Description Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you get familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into modelfree Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in real-world applications such as scientific research. Toward the end, you’ll gain a sense of what’s in store for reinforcement learning. By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow. What you will learn * Explore the fundamentals of reinforcement learning and the state-action-reward process * Understand Markov decision processes * Get well versed with libraries such as Keras, and TensorFlow * Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym * Choose and optimize a Q-Network’s learning parameters and fine-tune its performance * Discover real-world applications and use cases of Q-learning Who this book is for If you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.