Build your own hybrid cloud strategy with this comprehensive learning guide. About This Book ? Build a hybrid cloud strategy for your organization with AWS and OpenStack ? Leverage Hybrid Cloud to design a complex deployment pipeline ? Learn to implement security and monitoring best practices with real-world examples Who This Book Is For This book is targeted at cloud architects, cloud solution providers, DevOps engineers, or any working stakeholder who wants to learn about the hybrid cloud architecture. A basic understanding of public and private cloud is desirable. What You Will Learn ? Learn the demographics and definitions of Hybrid Cloud ? Understand the different architecture and design of Hybrid Cloud ? Explore multi-cloud strategy and use it with your hybrid cloud ? Implement a Hybrid Cloud using CMP / Common API’s ? Implement a Hybrid Cloud using Containers ? Overcome various challenges and issues while working with your Hybrid Cloud ? Understand how to monitor your Hybrid Cloud ? Discover the security implications in the Hybrid Cloud In Detail Hybrid cloud is currently the buzz word in the cloud world. Organizations are planning to adopt hybrid cloud strategy due to its advantages such as untested workloads, cloud-bursting, cloud service brokering and so on. This book will help you understand the dynamics, design principles, and deployment strategies of a Hybrid Cloud. You will start by understanding the concepts of hybrid cloud and the problems it solves as compared to a stand-alone public and private cloud. You will be delving into the different architecture and design of hybrid cloud. The book will then cover advanced concepts such as building a deployment pipeline, containerization strategy, and data storage mechanism. Next up, you will be able to deploy an external CMP to run a Hybrid cloud and integrate it with your OpenStack and AWS environments. You will also understand the strategy for designing a Hybrid Cloud using containerization and work with pre-built solutions like vCloud Air, VMware for AWS, and Azure Stack. Finally, the book will cover security and monitoring related best practices that will help you secure your cloud infrastructure. By the end of the book, you will be in a position to build a hybrid cloud strategy for your organization. Style and approach This book follows a step-by-step, practical approach to deploying and implementing hybrid cloud architectures using AWS and OpenStack.

Build neural network models in text, vision and advanced analytics using PyTorch About This Book ? Learn PyTorch for implementing cutting-edge deep learning algorithms. ? Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; ? Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Who This Book Is For This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected. What You Will Learn ? Use PyTorch for GPU-accelerated tensor computations ? Build custom datasets and data loaders for images and test the models using torchvision and torchtext ? Build an image classifier by implementing CNN architectures using PyTorch ? Build systems that do text classification and language modeling using RNN, LSTM, and GRU ? Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning ? Learn how to mix multiple models for a powerful ensemble model ? Generate new images using GAN’s and generate artistic images using style transfer In Detail Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease. Style and approach An end-to-end guide that teaches you all about PyTorch and how to implement it in various scenarios.

A practical guide filled with case studies and easy solutions to solve the most common user experience issues About This Book ? Understand and fix the pain points of a bad UX design to ensure greater customer satisfaction. ? Correct UX issues at various stages of a UX Design with the help of different methodologies for fixing bad UXs ? See best practices and established principles in UX with case studies illustrating these practices and principles Who This Book Is For This book is for anyone confronted with a poorly designed UX. It is ideal for UX professionals who want to solve problems with existing UX designs, and UX designers who want to enhance their designs or analyze and rectify where they went wrong. What You Will Learn ? Learn about ROI and metrics in UX ? Understand the importance of getting stakeholders involved ? Learn through real cases how to fix bad UX ? Identify and fix UX issues using different methodologies ? Learn how to turn insights and finding into practical UX solutions ? Learn to validate, test and measure the UX solutions implemented ? Learn about UX refactoring In Detail Have your web applications been experiencing more hits and less conversions? Are bad designs consuming your time and money? This book is the answer to these problems. With intuitive case studies, you’ll learn to simplify, fix, and enhance some common, real-world application designs. You’ll look at the common issues of simplicity, navigation, appearance, maintenance, and many more. The challenge that most UX designers face is to ensure that the UX is user-friendly. In this book, we address this with individual case studies starting with some common UX applications and then move on to complex applications. Each case study will help you understand the issues faced by a bad UX and teach you to break it down and fix these problems. As we progress, you’ll learn about the information architecture, usability testing, iteration, UX refactoring, and many other related features with the help of various case studies. You’ll also learn some interesting UX design tools with the projects covered in the book. By the end of the book, you’ll be armed with the knowledge to fix bad UX designs and to ensure great customer satisfaction for your applications. Style and approach This book takes a practical approach, with case studies, best practices, and practical solutions to bad design

Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on manner About This Book ? Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide ? Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more ? Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examples Who This Book Is For This book targets data scientists and machine learning developers who wish to get started with deep learning. If you know what deep learning is but are not quite sure of how to use it, this book will help you as well. An understanding of statistics and data science concepts is required. Some familiarity with Python programming will also be beneficial. What You Will Learn ? Understand the fundamentals of deep learning and how it is different from machine learning ? Get familiarized with Tensorflow, one of the most popular libraries for advanced machine learning ? Increase the predictive power of your model using feature engineering ? Understand the basics of deep learning by solving a digit classification problem of MNIST ? Demonstrate face generation based on the CelebA database, a promising application of generative models ? Apply deep learning to other domains like language modeling, sentiment analysis, and machine translation In Detail Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence. Style and approach A step-by-step guide filled with multiple examples to help you get started with data science and deep learning.

Join the virtual reality revolution by creating immersive 3D games and applications with Cardboard VR, Gear VR, OculusVR, and HTC Vive About This Book ? Develop robust, immersive VR experiences that are easy on the eye. ? Code 3D games and applications using Unity 3D game engine. ? Learn the basic principles of virtual reality applications Who This Book Is For If you are a game developer and a VR enthusiast now looking to get stuck into the VR app development process by creating VR apps for different platforms, then this is the book for you. Familiarity with the Unity game engine and the C# language is key to getting the most from this book. What You Will Learn ? Use Unity assets to create object simulation. ? Implement simple touch controls in your application. ? Apply artificial intelligence to achieve player and character interaction. ? Add *s for movement, tracking, grasping, and spawning. ? Create animated walkthroughs, use 360-degree media, and build engaging VR experiences. ? Deploy your games on multiple VR platforms. In Detail Are you new to virtual reality? Do you want to create exciting interactive VR applications? There's no need to be daunted by the thought of creating interactive VR applications, it's much easier than you think with this hands-on, project-based guide that will take you through VR development essentials for desktop and mobile-based games and applications. Explore the three top platforms—Cardboard VR, Gear VR, and OculusVR —to design immersive experiences from scratch. You’ll start by understanding the science-fiction roots of virtual reality and then build your first VR experience using Cardboard VR. You'll then delve into user interactions in virtual space for the Google Cardboard then move on to creating a virtual gallery with Gear VR. Then you will learn all about virtual movements, state machines, and spawning while you shoot zombies in the Oculus Rift headset. Next, you'll construct a Carnival Midway, complete with two common games to entertain players. Along the way, you will explore the best practices for VR development, review game design tips, discuss methods for combating motion sickness and identify alternate uses for VR applications Style and approach A project-based guide with every project built across chapters.

Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services Key Features * Implement data science and machine learning techniques to draw insights from real-world data * Understand what IBM Cloud platform can help you to implement cognitive insights within applications * Understand the role of data representation and feature extraction in any machine learning system Book Description IBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies. By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples. What you will learn * Understand key characteristics of IBM machine learning services * Run supervised and unsupervised techniques in the cloud * Understand how to create a Spark pipeline in Watson Studio * Implement deep learning and neural networks on the IBM Cloud with TensorFlow * Create a complete, cloud-based facial expression classification solution * Use biometric traits to build a cloud-based human identification system Who this book is for This beginner-level book is for data scientists and machine learning engineers who want to get started with IBM Cloud and its machine learning services using practical examples. Basic knowledge of Python and some understanding of machine learning will be useful.

Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key Features * Discover neural network architectures (like CNN and LSTM) that are driving recent advancements in AI * Build expert neural networks in Python using popular libraries such as Keras * Includes projects such as object detection, face identification, sentiment analysis, and more Book Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio. What you will learn * Learn various neural network architectures and its advancements in AI * Master deep learning in Python by building and training neural network * Master neural networks for regression and classification * Discover convolutional neural networks for image recognition * Learn sentiment analysis on textual data using Long Short-Term Memory * Build and train a highly accurate facial recognition security system Who this book is for This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.

Write efficient, reusable scripts to build custom characters, game environments, and control enemy AI Key Features * Build captivating multiplayer games using Unreal Engine and C++ * Incorporate existing C++ libraries into your game to add extra functionality such as hardware integration * Practical solutions for memory management, error handling, inputs, and collision for your game codebase Book Description Unreal Engine 4 (UE4) is a popular and award-winning game engine that powers some of the most popular games. A truly powerful tool for game development, there has never been a better time to use it for both commercial and independent projects. With more than 100 recipes, this book shows how to unleash the power of C++ while developing games with Unreal Engine. This book takes you on a journey to jumpstart your C++ and UE4 development skills. You will start off by setting up UE4 for C++ development and learn how to work with Visual Studio, a popular code editor. You will learn how to create C++ classes and structs the Unreal way. This will be followed by exploring memory management, smart pointers, and debugging your code. You will then learn how to make your own Actors and Components through code and how to handle input and collision events. You will also get exposure to many elements of game development including creating user interfaces, artificial intelligence, and writing code with networked play in mind. You will also learn how to add on to the Unreal Editor itself. With a range of task-oriented recipes, this book provides actionable information about writing code for games with UE4 using C++. By the end of the book, you will be empowered to become a top-notch developer with UE4 using C++ as your scripting language! What you will learn * Create C++ classes and structs that integrate well with UE4 and the Blueprints editor * Discover how to work with various APIs that Unreal Engine already contains * Utilize advanced concepts such as events, delegates, and interfaces in your UE4 projects * Build user interfaces using Canvas and UMG through C++ * Extend the Unreal Editor by creating custom windows and editors * Implement AI tasks and services using C++, Blackboard, and Behavior Trees * Write C++ code with networking in mind and replicate properties and functions Who this book is for If you are really passionate game developer looking for solutions to common scripting problems, then this is the book for you. Understanding of the fundamentals of game design and C++ is expected to get the most from this book.

Your one stop guide to automating infrastructure security using DevOps and DevSecOps Key Features * Secure and automate techniques to protect web, mobile or cloud services * Automate secure code inspection in C++, Java, Python, and JavaScript * Integrate security testing with automation frameworks like fuzz, BDD, Selenium and Robot Framework Book Description Security automation is the automatic handling of software security assessments tasks. This book helps you to build your security automation framework to scan for vulnerabilities without human intervention. This book will teach you to adopt security automation techniques to continuously improve your entire software development and security testing. You will learn to use open source tools and techniques to integrate security testing tools directly into your CI/CD framework. With this book, you will see how to implement security inspection at every layer, such as secure code inspection, fuzz testing, Rest API, privacy, infrastructure security, and web UI testing. With the help of practical examples, this book will teach you to implement the combination of automation and Security in DevOps. You will learn about the integration of security testing results for an overall security status for projects. By the end of this book, you will be confident implementing automation security in all layers of your software development stages and will be able to build your own in-house security automation platform throughout your mobile and cloud releases. What you will learn * Automate secure code inspection with open source tools and effective secure code scanning suggestions * Apply security testing tools and automation frameworks to identify security vulnerabilities in web, mobile and cloud services * Integrate security testing tools such as OWASP ZAP, NMAP, SSLyze, SQLMap, and OpenSCAP * Implement automation testing techniques with Selenium, JMeter, Robot Framework, Gauntlt, BDD, DDT, and Python unittest * Execute security testing of a Rest API Implement web application security with open source tools and script templates for CI/CD integration * Integrate various types of security testing tool results from a single project into one dashboard Who this book is for The book is for software developers, architects, testers and QA engineers who are looking to leverage automated security testing techniques.

Create a real-world application in Go and explore various frameworks and methodologies for full-stack development Key Features * Organize your isomorphic codebase to enhance the maintainability of your application * Build web APIs and middleware in the Go language by making use of the popular Gin framework * Implement real-time web application functionality with WebSockets Book Description The Go programming language has been rapidly adopted by developers for building web applications. With its impressive performance and ease of development, Go enjoys the support of a wide variety of open source frameworks, for building scalable and high-performant web services and apps. Hands-On Full Stack Development with Go is a comprehensive guide that covers all aspects of full stack development with Go. This clearly written, example-rich book begins with a practical exposure to Go development and moves on to build a frontend with the popular React framework. From there, you will build RESTful web APIs utilizing the Gin framework. After that, we will dive deeper into important software backend concepts, such as connecting to the database via an ORM, designing routes for your services, securing your services, and even charging credit cards via the popular Stripe API. We will also cover how to test, and benchmark your applications efficiently in a production environment. In the concluding chapters, we will cover isomorphic developments in pure Go by learning about GopherJS. As you progress through the book, you'll gradually build a musical instrument online store application from scratch. By the end of the book, you will be confident in taking on full stack web applications in Go. What you will learn * Understand Go programming by building a real-world application * Learn the React framework to develop a frontend for your application * Understand isomorphic web development utilizing the GopherJS framework * Explore methods to write RESTful web APIs in Go using the Gin framework * Learn practical topics such as ORM layers, secure communications, and Stripe's API * Learn methods to benchmark and test web APIs in Go Who this book is for Hands-On Full Stack Development with Go will appeal to developers who are looking to start building amazing full stack web applications in Go. Basic knowhow of Go language and JavaScript is expected. The book targets web developers who are looking to move to the Go language.

Your one-stop guide to learning and implementing artificial neural networks with Keras effectively Key Features * Design and create neural network architectures on different domains using Keras * Integrate neural network models in your applications using this highly practical guide * Get ready for the future of neural networks through transfer learning and predicting multi network models Book Description Neural networks are used to solve a wide range of problems in different areas of AI and deep learning. Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks. By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization. What you will learn * Understand the fundamental nature and workflow of predictive data modeling * Explore how different types of visual and linguistic signals are processed by neural networks * Dive into the mathematical and statistical ideas behind how networks learn from data * Design and implement various neural networks such as CNNs, LSTMs, and GANs * Use different architectures to tackle cognitive tasks and embed intelligence in systems * Learn how to generate synthetic data and use augmentation strategies to improve your models * Stay on top of the latest academic and commercial developments in the field of AI Who this book is for This book is for machine learning practitioners, deep learning researchers and AI enthusiasts who are looking to get well versed with different neural network architecture using Keras. Working knowledge of Python programming language is mandatory.

Get ready for the Windows 10: 70-698 exam and configure Windows to manage data recovery Key Features * Implement Windows 10 operational and administrative tasks * Configure devices, remote management settings, advanced management tools, and device drivers * Comprehensive guide to help you work efficiently in Windows 10 Book Description The Installing and Configuring Windows 10: 70-698 Exam Guide is designed to confirm what you already know, while also updating your knowledge of Windows 10. With its easy-to-follow guidance, you will quickly learn the user interface and discover steps to work efficiently in Windows 10 to rule out delays and obstacles. This book begins by covering various ways of installing Windows 10, followed by instructions on post-installation tasks. You will learn about the deployment of Windows 10 in Enterprise and also see how to configure networking in Windows 10. You’ll understand how to leverage Disk Management and Windows PowerShell to configure disks, volumes, and file system options. As you progress through the chapters, you will be able to set up remote management in Windows 10 and learn more about Windows update usage, behavior, and settings. You will also gain insights that will help you monitor and manage data recovery and explore how to configure authentication, authorization, and advanced management tools in Windows 10. By the end of this book, you will be equipped with enough knowledge to take the 70-698 exam and explore different study methods to improve your chances of passing the exam with ease. What you will learn * Discover various ways of installing Windows 10 * Understand how to configure devices and device drivers * Configure and support IPv4 and IPv6 network settings * Troubleshoot storage and removable device issues * Get to grips with data access and usage * Explore the advanced management tools available in Windows 10 Who this book is for This book is for IT professionals who perform installation, configuration, general local management and maintenance of Windows 10 core services and are preparing to clear the Windows 10: 70-698 exam

Implement machine learning and deep learning methodologies to build smart, cognitive AI projects using Python Key Features * A go-to guide to help you master AI algorithms and concepts * 8 real-world projects tackling different challenges in healthcare, e-commerce, and surveillance * Use TensorFlow, Keras, and other Python libraries to implement smart AI applications Book Description This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI. By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle. What you will learn * Build an intelligent machine translation system using seq-2-seq neural translation machines * Create AI applications using GAN and deploy smart mobile apps using TensorFlow * Translate videos into text using CNN and RNN * Implement smart AI Chatbots, and integrate and extend them in several domains * Create smart reinforcement, learning-based applications using Q-Learning * Break and generate CAPTCHA using Deep Learning and Adversarial Learning Who this book is for This book is intended for data scientists, machine learning professionals, and deep learning practitioners who are ready to extend their knowledge and potential in AI. If you want to build real-life smart systems to play a crucial role in every complex domain, then this book is what you need. Knowledge of Python programming and a familiarity with basic machine learning and deep learning concepts are expected to help you get the most out of the book

Enhance your programming skills by learning the intricacies of object oriented programming in C# 8 Key Features * Understand the four pillars of OOP; encapsulation, inheritance, abstraction and polymorphism * Leverage the latest features of C# 8 including nullable reference types and Async Streams * Explore various design patterns, principles, and best practices in OOP Book Description Object-oriented programming (OOP) is a programming paradigm organized around objects rather than actions, and data rather than logic. With the latest release of C#, you can look forward to new additions that improve object-oriented programming. This book will get you up to speed with OOP in C# in an engaging and interactive way. The book starts off by introducing you to C# language essentials and explaining OOP concepts through simple programs. You will then go on to learn how to use classes, interfacesm and properties to write pure OOP code in your applications. You will broaden your understanding of OOP further as you delve into some of the advanced features of the language, such as using events, delegates, and generics. Next, you will learn the secrets of writing good code by following design patterns and design principles. You'll also understand problem statements with their solutions and learn how to work with databases with the help of ADO.NET. Further on, you'll discover a chapter dedicated to the Git version control system. As you approach the conclusion, you'll be able to work through OOP-specific interview questions and understand how to tackle them. By the end of this book, you will have a good understanding of OOP with C# and be able to take your skills to the next level. What you will learn * Master OOP paradigm fundamentals * Explore various types of exceptions * Utilize C# language constructs efficiently * Solve complex design problems by understanding OOP * Understand how to work with databases using ADO.NET * Understand the power of generics in C# * Get insights into the popular version control system, Git * Learn how to model and design your software Who this book is for This book is designed for people who are new to object-oriented programming. Basic C# skills are assumed, however, prior knowledge of OOP in any other language is not required.

Go beyond the basics and unleash the full power of QGIS 3.4 and 3.6 with practical, step-by-step examples Key Features * One-stop solution to all of your GIS needs * Master QGIS by learning about database integration, and geoprocessing tools * Learn about the new and updated Processing toolbox and perform spatial analysis Book Description QGIS is an open source solution to GIS and widely used by GIS professionals all over the world. It is the leading alternative to proprietary GIS software. Although QGIS is described as intuitive, it is also, by default, complex. Knowing which tools to use and how to apply them is essential to producing valuable deliverables on time. Starting with a refresher on the QGIS basics and getting you acquainted with the latest QGIS 3.6 updates, this book will take you all the way through to teaching you how to create a spatial database and a GeoPackage. Next, you will learn how to style raster and vector data by choosing and managing different colors. The book will then focus on processing raster and vector data. You will be then taught advanced applications, such as creating and editing vector data. Along with that, you will also learn about the newly updated Processing Toolbox, which will help you develop the advanced data visualizations. The book will then explain to you the graphic modeler, how to create QGIS plugins with PyQGIS, and how to integrate Python analysis scripts with QGIS. By the end of the book, you will understand how to work with all aspects of QGIS and will be ready to use it for any type of GIS work. What you will learn * Create and manage a spatial database * Get to know advanced techniques to style GIS data * Prepare both vector and raster data for processing * Add heat maps, live layer effects, and labels to your maps * Master LAStools and GRASS integration with the Processing Toolbox * Edit and repair topological data errors * Automate workflows with batch processing and the QGIS Graphical Modeler * Integrate Python scripting into your data processing workflows * Develop your own QGIS plugins Who this book is for If you are a GIS professional, a consultant, a student, or perhaps a fast learner who wants to go beyond the basics of QGIS, then this book is for you. It will prepare you to realize the full potential of QGIS.

Gain basic skills in network forensics and learn how to apply them effectively Key Features * Investigate network threats with ease * Practice forensics tasks such as intrusion detection, network analysis, and scanning * Learn forensics investigation at the network level Book Description Network forensics is a subset of digital forensics that deals with network attacks and their investigation. In the era of network attacks and malware threat, it’s now more important than ever to have skills to investigate network attacks and vulnerabilities. Hands-On Network Forensics starts with the core concepts within network forensics, including coding, networking, forensics tools, and methodologies for forensic investigations. You’ll then explore the tools used for network forensics, followed by understanding how to apply those tools to a PCAP file and write the accompanying report. In addition to this, you will understand how statistical flow analysis, network enumeration, tunneling and encryption, and malware detection can be used to investigate your network. Towards the end of this book, you will discover how network correlation works and how to bring all the information from different types of network devices together. By the end of this book, you will have gained hands-on experience of performing forensics analysis tasks. What you will learn * Discover and interpret encrypted traffic * Learn about various protocols * Understand the malware language over wire * Gain insights into the most widely used malware * Correlate data collected from attacks * Develop tools and custom scripts for network forensics automation Who this book is for The book targets incident responders, network engineers, analysts, forensic engineers and network administrators who want to extend their knowledge from the surface to the deep levels of understanding the science behind network protocols, critical indicators in an incident and conducting a forensic search over the wire.

Solve real-world data problems with R and machine learning Key Features * Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.5 and beyond * Harness the power of R to build flexible, effective, and transparent machine learning models * Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R. What you will learn * Discover the origins of machine learning and how exactly a computer learns by example * Prepare your data for machine learning work with the R programming language * Classify important outcomes using nearest neighbor and Bayesian methods * Predict future events using decision trees, rules, and support vector machines * Forecast numeric data and estimate financial values using regression methods * Model complex processes with artificial neural networks — the basis of deep learning * Avoid bias in machine learning models * Evaluate your models and improve their performance * Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow Who this book is for Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.

Implement defensive techniques in your ecosystem successfully with Python Key Features * Identify and expose vulnerabilities in your infrastructure with Python * Learn custom exploit development . * Make robust and powerful cybersecurity tools with Python Book Description With the current technological and infrastructural shift, penetration testing is no longer a process-oriented activity. Modern-day penetration testing demands lots of automation and innovation; the only language that dominates all its peers is Python. Given the huge number of tools written in Python, and its popularity in the penetration testing space, this language has always been the first choice for penetration testers. Hands-On Penetration Testing with Python walks you through advanced Python programming constructs. Once you are familiar with the core concepts, you’ll explore the advanced uses of Python in the domain of penetration testing and optimization. You’ll then move on to understanding how Python, data science, and the cybersecurity ecosystem communicate with one another. In the concluding chapters, you’ll study exploit development, reverse engineering, and cybersecurity use cases that can be automated with Python. By the end of this book, you’ll have acquired adequate skills to leverage Python as a helpful tool to pentest and secure infrastructure, while also creating your own custom exploits. What you will learn * Get to grips with Custom vulnerability scanner development * Familiarize yourself with web application scanning automation and exploit development * Walk through day-to-day cybersecurity scenarios that can be automated with Python * Discover enterprise-or organization-specific use cases and threat-hunting automation * Understand reverse engineering, fuzzing, buffer overflows , key-logger development, and exploit development for buffer overflows. * Understand web scraping in Python and use it for processing web responses * Explore Security Operations Centre (SOC) use cases * Get to understand Data Science, Python, and cybersecurity all under one hood Who this book is for If you are a security consultant , developer or a cyber security enthusiast with little or no knowledge of Python and want in-depth insight into how the pen-testing ecosystem and python combine to create offensive tools , exploits , automate cyber security use-cases and much more then this book is for you. Hands-On Penetration Testing with Python guides you through the advanced uses of Python for cybersecurity and pen-testing, helping you to better understand security loopholes within your infrastructure .

Be an adaptive thinker that leads the way to Artificial Intelligence About This Book ? AI-based examples to guide you in designing and implementing machine intelligence ? Develop your own method for future AI solutions ? Acquire advanced AI, machine learning, and deep learning design skills Who This Book Is For Artificial Intelligence by Example is a simple, explanatory, and descriptive guide for junior developers, experienced developers, technology consultants, and those interested in AI who want to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this book. What You Will Learn ? Use adaptive thinking to solve real-life AI case studies ? Rise beyond being a modern-day factory code worker ? Acquire advanced AI, machine learning, and deep learning designing skills ? Learn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technology ? Understand future AI solutions and adapt quickly to them ? Develop out-of-the-box thinking to face any challenge the market presents In Detail Artificial Intelligence has the potential to replicate humans in every field. This book serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies. Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks. You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own. By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop business vision. Style and approach An easy-to-follow step by step guide which will help you get to grips with real world application of Artificial Intelligence

Get a solid understanding of Java fundamentals to master programming through a series of practical steps About This Book ? Enjoy your first step into the world of programming ? Understand what a language is and use its features to build applications ? Learn about a wide variety of programming applications Who This Book Is For Introduction to Programming is for anybody who wants to learn programming. All you’ll need is a computer, internet connection, and a cup of coffee. What You Will Learn ? Understand what Java is ? Install Java and learn how to run it ? Write and execute a Java program ? Write and execute the test for your program ? Install components and confgure your development environment ? Learn and use Java language fundamentals ? Learn object-oriented design principles ? Master the frequently used Java constructs In Detail Have you ever thought about making your computer do what you want it to do? Do you want to learn to program, but just don't know where to start? Instead of guiding you in the right direction, have other learning resources got you confused with over-explanations? Don't worry. Look no further. Introduction to Programming is here to help. Written by an industry expert who understands the challenges faced by those from a non-programming background, this book takes a gentle, hand-holding approach to introducing you to the world of programming. Beginning with an introduction to what programming is, you'll go on to learn about languages, their syntax, and development environments. With plenty of examples for you to code alongside reading, the book's practical approach will help you to grasp everything it has to offer. More importantly, you'll understand several aspects of application development. As a result, you'll have your very own application running by the end of the book. To help you comprehensively understand Java programming, there are exercises at the end of each chapter to keep things interesting and encourage you to add your own personal touch to the code and, ultimately, your application. Style and approach This step-by-step guide will familiarize you with programming using some practical examples.

Learn how to automate and manage your containers and reduce the overall operation burden on your system. About This Book ? Use containers to manage, scale and orchestrate apps in your organization ? Transform the latest concept of Kubernetes 1.10 into examples ? Expert techniques for orchestrating containers effectively Who This Book Is For This book is for system administrators, developers, DevOps engineers, or any stakeholder who wants to understand how Kubernetes works using a recipe-based approach. Basic knowledge of Kubernetes and Containers is required. What You Will Learn ? Build your own container cluster ? Deploy and manage highly scalable, containerized applications with Kubernetes ? Build high-availability Kubernetes clusters ? Build a continuous delivery pipeline for your application ? Track metrics and logs for every container running in your cluster ? Streamline the way you deploy and manage your applications with large-scale container orchestration In Detail Kubernetes is an open source orchestration platform to manage containers in a cluster environment. With Kubernetes, you can configure and deploy containerized applications easily. This book gives you a quick brush up on how Kubernetes works with containers, and an overview of main Kubernetes concepts, such as Pods, Deployments, Services and etc. This book explains how to create Kubernetes clusters and run applications with proper authentication and authorization configurations. With real-world recipes, you'll learn how to create high availability Kubernetes clusters on AWS, GCP and in on-premise datacenters with proper logging and monitoring setup. You'll also learn some useful tips about how to build a continuous delivery pipeline for your application. Upon completion of this book, you will be able to use Kubernetes in production and will have a better understanding of how to manage containers using Kubernetes. Style and approach This recipe-based book will teach you how to use Kubernetes in production and will help you discover various steps involved in managing your containers using Kubernetes