Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time Key Features *Make a hands-on start in the fields of Big Data, Distributed Technologies and Machine Learning *Learn how to design, develop and interpret the results of common Machine Learning algorithms *Uncover hidden patterns in your data in order to derive real actionable insights and business value Book Description Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data. What you will learn *Understand how Spark fits in the context of the big data ecosystem *Understand how to deploy and configure a local development environment using Apache Spark *Understand how to design supervised and unsupervised learning models *Build models to perform NLP, deep learning, and cognitive services using Spark ML libraries *Design real-time machine learning pipelines in Apache Spark *Become familiar with advanced techniques for processing a large volume of data by applying machine learning algorithms Who this book is for This book is aimed at Business Analysts, Data Analysts and Data Scientists who wish to make a hands-on start in order to take advantage of modern Big Data technologies combined with Advanced Analytics.

Gain in-depth knowledge of TypeScript and the latest ECMAScript standards by building robust web applications across different domains Key Features * Apply the cutting-edge features of TypeScript 3.0 to build high-performance, maintainable applications * Learn through practical examples of using TypeScript with popular frameworks, such as Angular and React * Focus on building high-quality applications that are modular, scalable and adaptable Book Description With the demand for ever more complex websites, the need to write robust, standard-compliant JavaScript has never been greater. TypeScript is modern JavaScript with the support of a first-class type system, which makes it simpler to write complex web systems. With this book, you’ll explore core concepts and learn by building a series of websites and TypeScript apps. You’ll start with an introduction to TypeScript features that are often overlooked in other books, before moving on to creating a simple markdown parser. You’ll then explore React and get up to speed with creating a client-side contacts manager. Next, the book will help you discover the Angular framework and use the MEAN stack to create a photo gallery. Later sections will assist you in creating a GraphQL Angular Todo app and then writing a Socket.IO chatroom. The book will also lead you through developing your final Angular project which is a mapping app. As you progress, you’ll gain insights into React with Docker and microservices. You’ll even focus on how to build an image classification program with machine learning using TensorFlow. Finally, you’ll learn to combine TypeScript and C# to create an ASP.NET Core-based music library app. By the end of this book, you’ll be able to confidently use TypeScript 3.0 and different JavaScript frameworks to build high-quality apps. What you will learn * Discover how to use TypeScript to write code using common patterns * Get to grips with using popular frameworks and libraries with TypeScript * Leverage the power of both server and client using TypeScript * Learn how to apply exciting new paradigms such as GraphQL and TensorFlow * Use popular cloud-based authenticated services * Combine TypeScript with C# to create ASP.NET Core applications Who this book is for This book is for programmers and web developers who are familiar with TypeScript and want to put their knowledge to work by building real-world complex applications. Prior experience with any other web framework is not required.

Explore various Generative Adversarial Network architectures using the Python ecosystem Key Features * Use different datasets to build advanced projects in the Generative Adversarial Network domain * Implement projects ranging from generating 3D shapes to a face aging application * Explore the power of GANs to contribute in open source research and projects Book Description Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain. Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an understanding of the architecture and functioning of generative models through their practical implementation. By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects. What you will learn * Train a network on the 3D ShapeNet dataset to generate realistic shapes * Generate anime characters using the Keras implementation of DCGAN * Implement an SRGAN network to generate high-resolution images * Train Age-cGAN on Wiki-Cropped images to improve face verification * Use Conditional GANs for image-to-image translation * Understand the generator and discriminator implementations of StackGAN in Keras Who this book is for If you’re a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.

Leverage Elastic Stack’s machine learning features to gain valuable insight from your data Key Features * Combine machine learning with the analytic capabilities of Elastic Stack * Analyze large volumes of search data and gain actionable insight from them * Use external analytical tools with your Elastic Stack to improve its performance Book Description Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly. What you will learn * Install the Elastic Stack to use machine learning features * Understand how Elastic machine learning is used to detect a variety of anomaly types * Apply effective anomaly detection to IT operations and security analytics * Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting * Combine your created jobs to correlate anomalies of different layers of infrastructure * Learn various tips and tricks to get the most out of Elastic machine learning Who this book is for If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.

A comprehensive guide that will give you hands-on experience to study and overcome financial cyber threats Key Features * Protect your financial environment with cybersecurity practices and methodologies * Identify vulnerabilities such as data manipulation and fraudulent transactions * Provide end-to-end protection within organizations Book Description Organizations have always been a target of cybercrime. Hands-On Cybersecurity for Finance teaches you how to successfully defend your system against common cyber threats, making sure your financial services are a step ahead in terms of security. The book begins by providing an overall description of cybersecurity, guiding you through some of the most important services and technologies currently at risk from cyber threats. Once you have familiarized yourself with the topic, you will explore specific technologies and threats based on case studies and real-life scenarios. As you progress through the chapters, you will discover vulnerabilities and bugs (including the human risk factor), gaining an expert-level view of the most recent threats. You'll then explore information on how you can achieve data and infrastructure protection. In the concluding chapters, you will cover recent and significant updates to procedures and configurations, accompanied by important details related to cybersecurity research and development in IT-based financial services. By the end of the book, you will have gained a basic understanding of the future of information security and will be able to protect financial services and their related infrastructures. What you will learn * Understand the cyber threats faced by organizations * Discover how to identify attackers * Perform vulnerability assessment, software testing, and pentesting * Defend your financial cyberspace using mitigation techniques and remediation plans * Implement encryption and decryption * Understand how Artificial Intelligence (AI) affects cybersecurity Who this book is for Hands-On Cybersecurity for Finance is for you if you are a security architect, cyber risk manager, or pentester looking to secure your organization. Basic understanding of cybersecurity tools and practices will help you get the most out of this book.

An in-depth and comprehensive take on WordPress, covering everything about the platform such as WordPress posts, pages, themes, plugins, and more. Key Features * Get up to date with the latest WordPress 5.0 (Bebo) and all its new features * Beginner-friendly layout and advice you can apply from day one with loads of screenshots and examples * Enrich your development experience with the new block-based editor Gutenberg Book Description Back in the day, when you wanted to launch a new website, you either had to learn web programming yourself or hire a professional who would take care of the whole process for you. Nowadays, with WordPress, anyone can build an optimized site with the least amount of effort possible and then make it available to the world in no time. Here, in the seventh edition of the book, we are going to show you how to build great looking and functional websites using WordPress. The new version of WordPress – 5.0 – comes with a few important changes, and we tell you all about how to use them effectively. From crafting content pages using the block editor, and customizing the design of your site, through to making sure it's secure, we go through it all. The book starts by introducing WordPress and teaching you how to set it up. You are then shown how to create a blog site, start writing content, and even use plugins and themes to customize the design of the site and add some unique elements to set it apart. If you want to get more in-depth, we also show you how to get started creating your own themes and plugins. Finally, we teach you how to use WordPress for building non-blog websites. By the end of the book, you will be sufficiently skilled to design high-quality websites and will be fully familiar with the ins and outs of WordPress. What you will learn * Learn to adapt your plugin with the Gutenberg editor * Create content that is optimized for publication on the web * Craft great looking pages and posts with the use of block editor * Structure your web pages in an accessible and clear way * Install and work with plugins and themes * Customize the design of your website * Upload multimedia content, such as images, audio, and video easily and effectively * Develop your own WordPress plugins and themes * Use WordPress to build websites that serve purposes other than blogs Who this book is for The ideal target audience for this book would be PHP developers who have some basic knowledge of working with WordPress and who want to get a comprehensive practical understanding of working with WordPress and create production-ready websites with it.

Compose and decompose ARM templates and use advanced concepts like looping, conditions, dependencies, PowerShell and Desired State Configuration. Key Features * Design, implement, and unit test ARM templates * Develop and deploy ARM templates following security best practices Book Description Azure Resource Manager (ARM) templates are declarations of Azure resources in the JSON format to provision and maintain them using infrastructure as code. This book gives practical solutions and examples for provisioning and managing various Azure services using ARM templates. The book starts with an understanding of infrastructure as code, a refresher on JSON, and then moves on to explain the fundamental concepts of ARM templates. Important concepts like iteration, conditional evaluation, security, usage of expressions, and functions will be covered in detail. You will use linked and nested templates to create modular ARM templates. You will see how to create multiple instances of the same resources, how to nest and link templates, and how to establish dependencies between them. You will also learn about implementing design patterns, secure template design, the unit testing of ARM templates, and adopting best practices. By the end of this book, you will understand the entire life cycle of ARM templates and their testing, and be able to author them for complex deployments. What you will learn * Understand the foundations of ARM templates including nested and linked templates * Design, create, and unit test ARM templates using best practices * Learn about conditional deployments, looping, Custom Script Extensions using PowerShell, Bash, and DSC * Implement design patterns related to ARM templates * Run post-deployment PowerShell and Desired State Configuration scripts * Create solutions and deploy them on Azure using ARM templates Who this book is for This books is for developers, DevOps engineers, and architects who have experience in Azure.

Unreal Engine 4 for virtual reality game design, development, User Experience design techniques and Blueprint programming to create virtual reality gameplay for HTC Vive, Oculus Rift, PSVR, and Windows Mixed Reality headsets. Key Features * Build VR games from scratch with the power of Unreal Engine 4 * Learn User Experience design practices to take your VR game to the next level * Understand the best practices to creating art for games on HTC Vive, Oculus Rift, and PSVR Book Description With the ability to put players directly in the game, virtual reality gives users the chance to experience digital worlds directly. Nevertheless, many designers are unsure where to start when working with this amazing technology. With this book, you will learn user experience design processes and create immersive gameplay experiences designed for entertainment and player comfort. Using the power of Unreal Engine 4’s Blueprint visual scripting language, you will build player interaction and locomotion systems from scratch and use these flexible systems to create a sample game, as well as develop functional 2D and 3D user interfaces for players to interact with. And also learn the best practices for creating game art for virtual reality. Finally, you will learn how to test your application with your target audience and finalize your game for distribution. By the end of this book, you will have the knowledge to be able to make the leap from traditional game development to creating immersive virtual reality experiences using Unreal Engine 4. What you will learn * Understand how to get started with VR development in Unreal Engine 4 * Design and create interaction and locomotion systems from scratch * Plan and program a sample game for VR * Understand how VR affects user experience and user interfaces * Discuss what is needed to create optimized art for VR * Test your game with users and prepare for distribution Who this book is for The audience for this book is intermediate or advanced users of Unreal Engine 4 but who have not begun working with VR technology. These users are familiar with the game engine and have an interest in VR technology. They are just beginning to explore the VR features that the game engine has to offer.

Build efficient forecasting models using traditional time series models and machine learning algorithms. Key Features * Perform time series analysis and forecasting using R packages such as Forecast and h2o * Develop models and find patterns to create visualizations using the TSstudio and plotly packages * Master statistics and implement time-series methods using examples mentioned Book Description Time series analysis is the art of extracting meaningful insights from, and revealing patterns in, time series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models. You will learn how to preprocess raw time series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo. You will analyze data and extract meaningful information from it using both descriptive statistics and rich data visualization tools in R such as the TSstudio, plotly, and ggplot2 packages. The later section of the book delves into traditional forecasting models such as time series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You'll also cover advanced time series regression models with machine learning algorithms such as Random Forest and Gradient Boosting Machine using the h2o package. By the end of this book, you will have the skills needed to explore your data, identify patterns, and build a forecasting model using various traditional and machine learning methods. What you will learn * Visualize time series data and derive better insights * Explore auto-correlation and master statistical techniques * Use time series analysis tools from the stats, TSstudio, and forecast packages * Explore and identify seasonal and correlation patterns * Work with different time series formats in R * Explore time series models such as ARIMA, Holt-Winters, and more * Evaluate high-performance forecasting solutions Who this book is for Hands-On Time Series Analysis with R is ideal for data analysts, data scientists, and all R developers who are looking to perform time series analysis to predict outcomes effectively. A basic knowledge of statistics is required; some knowledge in R is expected, but not mandatory.

Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills Key Features * Speed up your data analysis projects using powerful R packages and techniques * Create multiple hands-on data analysis projects using real-world data * Discover and practice graphical exploratory analysis techniques across domains Book Description Hands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You'll also uncover the structure of your data, and you'll learn graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context. What you will learn * Learn powerful R techniques to speed up your data analysis projects * Import, clean, and explore data using powerful R packages * Practice graphical exploratory analysis techniques * Create informative data analysis reports using ggplot2 * Identify and clean missing and erroneous data * Explore data analysis techniques to analyze multi-factor datasets Who this book is for Hands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis.

Choose the smarter way to learn about containerizing your applications and running them in production. Key Features * Deploy and manage highly scalable, containerized applications with Kubernetes * Build high-availability Kubernetes clusters * Secure your applications via encapsulation, networks, and secrets Book Description Kubernetes is an open source orchestration platform for managing containers in a cluster environment. This Learning Path introduces you to the world of containerization, in addition to providing you with an overview of Docker fundamentals. As you progress, you will be able to understand how Kubernetes works with containers. Starting with creating Kubernetes clusters and running applications with proper authentication and authorization, you'll learn how to create high-availability Kubernetes clusters on Amazon Web Services (AWS), and also learn how to use kubeconfig to manage different clusters. Whether it is learning about Docker containers and Docker Compose, or building a continuous delivery pipeline for your application, this Learning Path will equip you with all the right tools and techniques to get started with containerization. By the end of this Learning Path, you will have gained hands-on experience of working with Docker containers and orchestrators, including SwarmKit and Kubernetes. This Learning Path includes content from the following Packt products: * Kubernetes Cookbook - Second Edition by Hideto Saito, Hui-Chuan Chloe Lee, and Ke-Jou Carol Hsu * Learn Docker - Fundamentals of Docker 18.x by Gabriel N. Schenker What you will learn * Build your own container cluster * Run a highly distributed application with Docker Swarm or Kubernetes * Update or rollback a distributed application with zero downtime * Containerize your traditional or microservice-based application * Build a continuous delivery pipeline for your application * Track metrics and logs for every container in your cluster * Implement container orchestration to streamline deploying and managing applications Who this book is for This beginner-level Learning Path is designed for system administrators, operations engineers, DevOps engineers, and developers who want to get started with Docker and Kubernetes. Although no prior experience with Docker is required, basic knowledge of Kubernetes and containers will be helpful.

Use self-driven data centers to reduce management complexity by deploying Infrastructure as Code to gain value from investments. Key Features * Add smart capabilities in VMware Workspace ONE to deliver customer insights and improve overall security * Optimize your HPC and big data infrastructure with the help of machine learning * Automate your VMware data center operations with machine learning Book Description This book presents an introductory perspective on how machine learning plays an important role in a VMware environment. It offers a basic understanding of how to leverage machine learning primitives, along with a deeper look into integration with the VMware tools used for automation today. This book begins by highlighting how VMware addresses business issues related to its workforce, customers, and partners with emerging technologies such as machine learning to create new, intelligence-driven, end user experiences. You will learn how to apply machine learning techniques incorporated in VMware solutions for data center operations. You will go through management toolsets with a focus on machine learning techniques. At the end of the book, you will learn how the new vSphere Scale-Out edition can be used to ensure that HPC, big data performance, and other requirements can be met (either through development or by fine-tuning guidelines) with mainstream products. What you will learn * Orchestrate on-demand deployments based on defined policies * Automate away common problems and make life easier by reducing errors * Deliver services to end users rather than to virtual machines * Reduce rework in a multi-layered scalable manner in any cloud * Explore the centralized life cycle management of hybrid clouds * Use common code so you can run it across any cloud Who this book is for This book is intended for those planning, designing, and implementing the virtualization/cloud components of the Software-Defined Data Center foundational infrastructure. It helps users to put intelligence in their automation tasks to get self driving data center. It is assumed that the reader has knowledge of, and some familiarity with, virtualization concepts and related topics, including storage, security, and networking.

Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch Key Features * Learn and implement machine learning algorithms in a variety of real-life scenarios * Cover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques * Find easy-to-follow code solutions for tackling common and not-so-common challenges Book Description This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples. What you will learn * Use predictive modeling and apply it to real-world problems * Explore data visualization techniques to interact with your data * Learn how to build a recommendation engine * Understand how to interact with text data and build models to analyze it * Work with speech data and recognize spoken words using Hidden Markov Models * Get well versed with reinforcement learning, automated ML, and transfer learning * Work with image data and build systems for image recognition and biometric face recognition * Use deep neural networks to build an optical character recognition system Who this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful.

Get close and comfortable with Unity and build applications that run on HoloLens, Daydream, and Oculus Rift Key Features * Build fun augmented reality applications using ARKit, ARCore, and Vuforia * Explore virtual reality by developing more than 10 engaging projects * Learn how to integrate AR and VR concepts together in a single application Book Description Unity is the leading platform to develop mixed reality experiences because it provides a great pipeline for working with 3D assets. Using a practical and project-based approach, this Learning Path educates you about the specifics of AR and VR development using Unity 2018 and Unity 3D. You’ll learn to integrate, animate, and overlay 3D objects on your camera feed, before moving on to implement sensor-based AR applications. You’ll explore various concepts by creating an AR application using Vuforia for both macOS and Windows for Android and iOS devices. Next, you’ll learn how to develop VR applications that can be experienced with devices, such as Oculus and Vive. You’ll also explore various tools for VR development: gaze-based versus hand controller input, world space UI canvases, locomotion and teleportation, timeline animation, and multiplayer networking. You’ll learn the Unity 3D game engine via the interactive Unity Editor and C# programming. By the end of this Learning Path, you’ll be fully equipped to develop rich, interactive mixed reality experiences using Unity. This Learning Path includes content from the following Packt products: * Unity Virtual Reality Projects - Second Edition by Jonathan Linowes * Unity 2018 Augmented Reality Projects by Jesse Glover What you will learn * Create 3D scenes to learn about world space and scale * Move around your scenes using locomotion and teleportation * Create filters or overlays that work with facial recognition software * Interact with virtual objects using eye gaze, hand controllers, and user input events * Design and build a VR storytelling animation with a soundtrack and timelines * Create social VR experiences with Unity networking Who this book is for If you are a game developer familiar with 3D computer graphics and interested in building your own AR and VR games or applications, then this Learning Path is for you. Any prior experience in Unity and C# will be an advantage. In all, this course teaches you the tools and techniques to develop engaging mixed reality applications.

Take your neural networks to a whole new level with the simplicity and modularity of Keras, the most commonly used high-level neural networks API. Key Features * Solve complex machine learning problems with precision * Evaluate, tweak, and improve your deep learning models and solutions * Use different types of neural networks to solve real-world problems Book Description Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code. Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model. By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks. What you will learn * Understand the difference between single-layer and multi-layer neural network models * Use Keras to build simple logistic regression models, deep neural networks, recurrent neural networks, and convolutional neural networks * Apply L1, L2, and dropout regularization to improve the accuracy of your model * Implement cross-validate using Keras wrappers with scikit-learn * Understand the limitations of model accuracy Who this book is for If you have basic knowledge of data science and machine learning and want to develop your skills and learn about artificial neural networks and deep learning, you will find this book useful. Prior experience of Python programming and experience with statistics and logistic regression will help you get the most out of this book. Although not necessary, some familiarity with the scikit-learn library will be an added bonus.

Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key Features * Study new techniques for marketing analytics * Explore uses of machine learning to power your marketing analyses * Work through each stage of data analytics with the help of multiple examples and exercises Book Description Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions. What you will learn * Analyze and visualize data in Python using pandas and Matplotlib * Study clustering techniques, such as hierarchical and k-means clustering * Create customer segments based on manipulated data * Predict customer lifetime value using linear regression * Use classification algorithms to understand customer choice * Optimize classification algorithms to extract maximal information Who this book is for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.

Enhance your programming skills to build exciting robotic projects Key Features * Build an intelligent robot that can detect and avoid obstacles and respond to voice commands * Detect and track objects and faces using OpenCV * Control your robot with a GUI button designed using Qt5 Book Description C++ is one of the most popular legacy programming languages for robotics, and a combination of C++ and robotics hardware is used in many leading industries. This book will bridge the gap between Raspberry Pi and C/C++ programming and enable you to develop applications for Raspberry Pi. To follow along with the projects covered in the book, you can implement C programs in Raspberry Pi with the wiringPi library. With this book, you’ll develop a fully functional car robot and write programs to move it in different directions. You’ll then create an obstacle - avoiding robot using an ultrasonic sensor. Furthermore, you’ll find out how to control the robot wirelessly using your PC/Mac. This book will also help you work with object detection and tracking using OpenCV, and guide you through exploring face detection techniques. Finally, you will create an Android app and control the robot wirelessly with an Android smartphone. By the end of this book, you will have gained experience in developing a robot using Raspberry Pi and C/C++ programming. What you will learn * Install software in Raspberry Pi compatible with C++ programming * Program the Raspberry Pi in C++ to run a motor * Control RPi-powered robot wirelessly with your laptop or PC * Program an RPi camera using OpenCV Control a Raspberry Pi robot with voice commands * Implement face and object detection with Raspberry Pi Who this book is for This book is for developers, programmers, and robotics enthusiasts interested in leveraging C++ to build exciting robotics applications. Prior knowledge of C++ is necessary to understand the projects covered in this book.

Enhance and secure your datacenter with the newest Microsoft server platform Key Features * Develop necessary skills to design and implement Microsoft Server 2019 in enterprise environment * Provide support to your medium to large enterprise and leverage your experience in administering Microsoft Server 2019 * Effectively administering Windows server 2019 with the help of practical examples Book Description Mastering Windows Server 2019 – Second Edition covers all of the essential information needed to implement and utilize this latest-and-greatest platform as the core of your data center computing needs. You will begin by installing and managing Windows Server 2019, and by clearing up common points of confusion surrounding the versions and licensing of this new product. Centralized management, monitoring, and configuration of servers is key to an efficient IT department, and you will discover multiple methods for quickly managing all of your servers from a single pane of glass. To this end, you will spend time inside Server Manager, PowerShell, and even the new Windows Admin Center, formerly known as Project Honolulu. Even though this book is focused on Windows Server 2019 LTSC, we will still discuss containers and Nano Server, which are more commonly related to the SAC channel of the server platform, for a well-rounded exposition of all aspects of using Windows Server in your environment. We also discuss the various remote access technologies available in this operating system, as well as guidelines for virtualizing your data center with Hyper-V. By the end of this book, you will have all the ammunition required to start planning for, implementing, and managing Windows. What you will learn * Work with the updated Windows Server 2019 interface, including Server Core and Windows Admin Center * Secure your network and data with new technologies in Windows Server 2019 * Learn about containers and understand the appropriate situations to use Nano Server * Discover new ways to integrate your data center with Microsoft Azure * Harden your Windows Servers to help keep the bad guys out * Virtualize your data center with Hyper-V Who this book is for If you are a System Administrator or an IT professional interested in designing and deploying Windows Server 2019 then this book is for you. Previous experience of Windows Server operating systems and familiarity with networking concepts is required.

Explore the power of D3.js 5 and its integration with web technologies for building rich and interactive data visualization solutions Key Features * Explore the latest D3.js 5 for creating charts, plots, and force-directed graphics * Practical guide for creating interactive graphics and data-driven apps with JavaScript * Build Real-time visualization and transition on web using SVG with D3.js Book Description This book is a practical hands-on introduction to D3 (Data-driven Documents): the most popular open-source JavaScript library for creating interactive web-based data visualizations. Based entirely on open web standards, D3 provides an integrated collection of tools for efficiently binding data to graphical elements. If you have basic knowledge of HTML, CSS and JavaScript you can use D3.js to create beautiful interactive web-based data visualizations. D3 is not a charting library. It doesn’t contain any pre-defined chart types, but can be used to create whatever visual representations of data you can imagine. The goal of this book is to introduce D3 and provide a learning path so that you obtain a solid understanding of its fundamental concepts, learn to use most of its modules and functions, and gain enough experience to create your own D3 visualizations. You will learn how to create bar, line, pie and scatter charts, trees, dendograms, treemaps, circle packs, chord/ribbon diagrams, sankey diagrams, animated network diagrams, and maps using different geographical projections. Fundamental concepts are explained in each chapter and then applied to a larger example in step-by-step tutorials, complete with full code, from hundreds of examples you can download and run. This book covers D3 version 5 and is based on ES2015 JavaScript. What you will learn * Learn to use D3.js version 5 and web standards to create beautiful interactive data-driven visualizations for the web * Bind data to DOM elements, applying different scales, color schemes and configuring smooth animated transitions for data updates * Generate data structures and layouts for many popular chart formats * Apply interactive behaviors to any chart * Create thematic maps based on GIS data using different geographical projections with interactive behaviors * Load, parse and transform data from JSON and CSV formats Who this book is for The book is intended for web developers, web designers, data scientists, artists, and any developer who wish to create interactive data visualization for the Web using D3. The book assumes basic knowledge of HTML, CSs, and JavaScript.

Explore distributed ledger technology, decentralization, and smart contracts and develop real-time decentralized applications with Ethereum and Solidity Key Features * Get to grips with the underlying technical principles and implementations of blockchain * Build powerful applications using Ethereum to secure transactions and create smart contracts * Gain advanced insights into cryptography and cryptocurrencies Book Description Blockchain technology is a distributed ledger with applications in industries such as finance, government, and media. This Learning Path is your guide to building blockchain networks using Ethereum, JavaScript, and Solidity. You will get started by understanding the technical foundations of blockchain technology, including distributed systems, cryptography and how this digital ledger keeps data secure. Further into the chapters, you’ll gain insights into developing applications using Ethereum and Hyperledger. As you build on your knowledge of Ether security, mining , smart contracts, and Solidity, you’ll learn how to create robust and secure applications that run exactly as programmed without being affected by fraud, censorship, or third-party interference. Toward the concluding chapters, you’ll explore how blockchain solutions can be implemented in applications such as IoT apps, in addition to its use in currencies. The Learning Path will also highlight how you can increase blockchain scalability and even discusses the future scope of this fascinating and powerful technology. By the end of this Learning Path, you'll be equipped with the skills you need to tackle pain points encountered in the blockchain life cycle and confidently design and deploy decentralized applications. This Learning Path includes content from the following Packt products: * Mastering Blockchain - Second Edition by Imran Bashir * Building Blockchain Projects by Narayan Prusty What you will learn * Understand why decentralized applications are important * Discover the mechanisms behind bitcoin and alternative cryptocurrencies * Master how cryptography is used to secure data with the help of examples * Maintain, monitor, and manage your blockchain solutions * Create Ethereum wallets * Explore research topics and the future scope of blockchain technology Who this book is for This Learning Path is designed for blockchain developers who want to build decentralized applications and smart contracts from scratch using Hyperledger. Basic familiarity with any programming language will be useful to get started with this Learning Path.

Build smarter systems by combining artificial intelligence and the Internet of Things—two of the most talked about topics today Key Features * Leverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT data * Process IoT data and predict outcomes in real time to build smart IoT models * Cover practical case studies on industrial IoT, smart cities, and home automation Book Description There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence. What you will learn * Apply different AI techniques including machine learning and deep learning using TensorFlow and Keras * Access and process data from various distributed sources * Perform supervised and unsupervised machine learning for IoT data * Implement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platforms * Forecast time-series data using deep learning methods * Implementing AI from case studies in Personal IoT, Industrial IoT, and Smart Cities * Gain unique insights from data obtained from wearable devices and smart devices Who this book is for If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how popular artificial intelligence (AI) techniques can be used in the Internet of Things domain, this book will also be of benefit. A basic understanding of machine learning concepts will be required to get the best out of this book.