Leverage PowerShell to perform many day-to-day tasks in Microsoft Azure Key Features *Deploy and manage Azure virtual machines with PowerShell commands. *Get to grips with core concept of Azure PowerShell such as working with images and disks, custom script extension, high availability and more. *Leverage hands-on projects to successfully apply what you learned through the course of this book. Book Description As an IT professional, it is important to keep up with cloud technologies and learn to manage those technologies. PowerShell is a critical tool that must be learned in order to effectively and more easily manage many Azure resources. This book is designed to teach you to leverage PowerShell to enable you to perform many day-to-day tasks in Microsoft Azure. Taking you through the basic tasks of installing Azure PowerShell and connecting to Azure, you will learn to properly connect to an Azure tenant with PowerShell. Next, you will dive into tasks such as deploying virtual machines with PowerShell, resizing them, and managing their power states with PowerShell. Then, you will learn how to complete more complex Azure tasks with PowerShell, such as deploying virtual machines from custom images, creating images from existing virtual machines, and creating and managing of data disks. Later, you will learn how to snapshot virtual machines, how to encrypt virtual machines, and how to leverage load balancers to ensure high availability with PowerShell. By the end of this book, you will have developed dozens of PowerShell skills that are invaluable in the deployment and management of Azure virtual machines. What you will learn *Manage virtual machines with PowerShell *Resize a virtual machine with PowerShell *Create OS disk snapshots via PowerShell *Deploy new virtual machines from snapshots via PowerShell *Provision and attach data disks to a virtual machine via PowerShell *Load balance virtual machines with PowerShell *Manage virtual machines with custom script extensions Who this book is for This book is intended for IT professionals who are responsible for managing Azure virtual machines. No prior PowerShell or Azure experience is needed.

Learn development skills and improve productivity when programming in Microsoft Dynamics NAV 2018 - the popular Enterprise Resourse Planning management system used across a variety of industries for business process management Key Features *Solve common business problems with the valuable features and flexibility of Dynamics NAV *Understand the structure of NAV database - how documents and business entities are mapped to DB tables *Design user interface and bind the presentation layer with the data storage Book Description Microsoft Dynamics NAV is an enterprise resource planning (ERP) software suite for organizations. The system offers specialized functionality for manufacturing, distribution, government, retail, and other industries. This book gets you started with its integrated development environment for solving problems by customizing business processes. This book introduces the NAV development environment – C/SIDE. It gives an overview of the internal system language and the most essential development tools. The book will enable the reader to customize and extend NAV functionality with C/AL code, design a user interface through pages, create role centers, and build advanced reports in Microsoft Visual Studio. By the end of the book, you will have learned how to extend the NAV data model, how to write and debug custom code, and how to exchange data with external applications. What you will learn *Manage NAV Server configuration with Microsoft Management Console *Manage NAV installation with the NAV Administration Shell *Create integration events and extend functionality via the NAV event model *Run XML Ports from C/AL code *Design reports and write client code in RDLC expressions Who this book is for This book is for experienced NAV users who have an understanding of basic programming concepts. Familiarity with NAV development environment or its internal development language-C/AL is not expected.

Architect scalable, reliable, and maintainable applications for enterprises with Python Key Features *Explore various Python design patterns used for enterprise software development *Apply best practices for testing and performance optimization to build stable applications *Learn about different attacking strategies used on enterprise applications and how to avoid them Book Description Dynamically typed languages like Python are continuously improving. With the addition of exciting new features and a wide selection of modern libraries and frameworks, Python has emerged as an ideal language for developing enterprise applications. Hands-On Enterprise Application Development with Python will show you how to build effective applications that are stable, secure, and easily scalable. The book is a detailed guide to building an end-to-end enterprise-grade application in Python. You will learn how to effectively implement Python features and design patterns that will positively impact your application lifecycle. The book also covers advanced concurrency techniques that will help you build a RESTful application with an optimized frontend. Given that security and stability are the foundation for an enterprise application, you’ll be trained on effective testing, performance analysis, and security practices, and understand how to embed them in your codebase during the initial phase. You’ll also be guided in how to move on from a monolithic architecture to one that is service oriented, leveraging microservices and serverless deployment techniques. By the end of the book, you will have become proficient at building efficient enterprise applications in Python. What you will learn *Understand the purpose of design patterns and their impact on application lifecycle *Build applications that can handle large amounts of data-intensive operations *Uncover advanced concurrency techniques and discover how to handle a large number of requests in production *Optimize frontends to improve the client-side experience of your application *Effective testing and performance profiling techniques to detect issues in applications early in the development cycle *Build applications with a focus on security *Implement large applications as microservices to improve scalability Who this book is for If you’re a developer who wants to build enterprise-grade applications, this book is for you. Basic to intermediate-level of programming experience with Python and database systems is required to understand the concepts covered in this book.

Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. Key Features *Bridge your data analysis with the power of programming, complex algorithms, and AI *Use Python and its extensive libraries to power your way to new levels of data insight *Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series *Explore this modern approach across with key industry case studies and hands-on projects Book Description Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. What you will learn *A new toolset that has been carefully crafted to meet for your data analysis challenges *Full and detailed case studies of the toolset across several of today’s key industry contexts *Become super productive with a new toolset across Python and Jupyter Notebook *Look into the future of data science and which directions to develop your skills next Who this book is for This book is for developers wanting to bridge the gap between them and data scientists. Introducing PixieDust from its creator, the book is a great desk companion for the accomplished Data Scientist. Some fluency in data interpretation and visualization is assumed. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development.

Speed up the design and implementation of deep learning solutions using Apache Spark Key Features * Explore the world of distributed deep learning with Apache Spark * Train neural networks with deep learning libraries such as BigDL and TensorFlow * Develop Spark deep learning applications to intelligently handle large and complex datasets Book Description Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases. What you will learn * Understand the basics of deep learning * Set up Apache Spark for deep learning * Understand the principles of distribution modeling and different types of neural networks * Obtain an understanding of deep learning algorithms * Discover textual analysis and deep learning with Spark * Use popular deep learning frameworks, such as Deeplearning4j, TensorFlow, and Keras * Explore popular deep learning algorithms Who this book is for If you are a Scala developer, data scientist, or data analyst who wants to learn how to use Spark for implementing efficient deep learning models, Hands-On Deep Learning with Apache Spark is for you. Knowledge of the core machine learning concepts and some exposure to Spark will be helpful.

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

Your hands-on reference guide to developing, training, and optimizing your machine learning models Key Features * Your guide to learning efficient machine learning processes from scratch * Explore expert techniques and hacks for a variety of machine learning concepts * Write effective code in R, Python, Scala, and Spark to solve all your machine learning problems Book Description Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference. What you will learn * Get a quick rundown of model selection, statistical modeling, and cross-validation * Choose the best machine learning algorithm to solve your problem * Explore kernel learning, neural networks, and time-series analysis * Train deep learning models and optimize them for maximum performance * Briefly cover Bayesian techniques and sentiment analysis in your NLP solution * Implement probabilistic graphical models and causal inferences * Measure and optimize the performance of your machine learning models Who this book is for If you’re a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you’re an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You’ll need some exposure to machine learning to get the best out of this book.

Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras Key Features * Get to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and Keras * Implement advanced concepts and popular machine learning algorithms in real-world projects * Build analytics, computer vision, and neural network projects Book Description Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects. The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks. By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects. What you will learn * Understand the Python data science stack and commonly used algorithms * Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window * Understand NLP concepts by creating a custom news feed * Create applications that will recommend GitHub repositories based on ones you’ve starred, watched, or forked * Gain the skills to build a chatbot from scratch using PySpark * Develop a market-prediction app using stock data * Delve into advanced concepts such as computer vision, neural networks, and deep learning Who this book is for This book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. The intermediate-level guide will help you to implement libraries from the Python ecosystem to build a variety of projects addressing various machine learning domains. Knowledge of Python programming and machine learning concepts will be helpful.

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

Power up your network applications with Python programming Key Features * Master Python skills to develop powerful network applications * Grasp the fundamentals and functionalities of SDN * Design multi-threaded, event-driven architectures for echo and chat servers Book Description This Learning Path highlights major aspects of Python network programming such as writing simple networking clients, creating and deploying SDN and NFV systems, and extending your network with Mininet. You’ll also learn how to automate legacy and the latest network devices. As you progress through the chapters, you’ll use Python for DevOps and open source tools to test, secure, and analyze your network. Toward the end, you'll develop client-side applications, such as web API clients, email clients, SSH, and FTP, using socket programming. By the end of this Learning Path, you will have learned how to analyze a network's security vulnerabilities using advanced network packet capture and analysis techniques. This Learning Path includes content from the following Packt products: * Practical Network Automation by Abhishek Ratan * Mastering Python Networking by Eric Chou * Python Network Programming Cookbook, Second Edition by Pradeeban Kathiravelu, Dr. M. O. Faruque Sarker What you will learn * Create socket-based networks with asynchronous models * Develop client apps for web APIs, including S3 Amazon and Twitter * Talk to email and remote network servers with different protocols * Integrate Python with Cisco, Juniper, and Arista eAPI for automation * Use Telnet and SSH connections for remote system monitoring * Interact with websites via XML-RPC, SOAP, and REST APIs * Build networks with Ryu, OpenDaylight, Floodlight, ONOS, and POX * Configure virtual networks in different deployment environments Who this book is for If you are a Python developer or a system administrator who wants to start network programming, this Learning Path gets you a step closer to your goal. IT professionals and DevOps engineers who are new to managing network devices or those with minimal experience looking to expand their knowledge and skills in Python will also find this Learning Path useful. Although prior knowledge of networking is not required, some experience in Python programming will be helpful for a better understanding of the concepts in the Learning Path.

Perform advanced dashboard, visualization, and analytical techniques with Tableau Desktop, Tableau Prep, and Tableau Server Key Features * Unique problem-solution approach to aid effective business decision-making * Create interactive dashboards and implement powerful business intelligence solutions * Includes best practices on using Tableau with modern cloud analytics services Book Description Tableau has been one of the most popular business intelligence solutions in recent times, thanks to its powerful and interactive data visualization capabilities. Tableau 2019.x Cookbook is full of useful recipes from industry experts, who will help you master Tableau skills and learn each aspect of Tableau's ecosystem. This book is enriched with features such as Tableau extracts, Tableau advanced calculations, geospatial analysis, and building dashboards. It will guide you with exciting data manipulation, storytelling, advanced filtering, expert visualization, and forecasting techniques using real-world examples. From basic functionalities of Tableau to complex deployment on Linux, you will cover it all. Moreover, you will learn advanced features of Tableau using R, Python, and various APIs. You will learn how to prepare data for analysis using the latest Tableau Prep. In the concluding chapters, you will learn how Tableau fits the modern world of analytics and works with modern data platforms such as Snowflake and Redshift. In addition, you will learn about the best practices of integrating Tableau with ETL using Matillion ETL. By the end of the book, you will be ready to tackle business intelligence challenges using Tableau's features. What you will learn * Understand the basic and advanced skills of Tableau Desktop * Implement best practices of visualization, dashboard, and storytelling * Learn advanced analytics with the use of build in statistics * Deploy the multi-node server on Linux and Windows * Use Tableau with big data sources such as Hadoop, Athena, and Spectrum * Cover Tableau built-in functions for forecasting using R packages * Combine, shape, and clean data for analysis using Tableau Prep * Extend Tableau’s functionalities with REST API and R/Python Who this book is for Tableau 2019.x Cookbook is for data analysts, data engineers, BI developers, and users who are looking for quick solutions to common and not-so-common problems faced while using Tableau products. Put each recipe into practice by bringing the latest offerings of Tableau 2019.x to solve real-world analytics and business intelligence challenges. Some understanding of BI concepts and Tableau is required.

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.

Leverage the power of MongoDB 4.x to build and administer fault-tolerant database applications Key Features * Master the new features and capabilities of MongoDB 4.x * Implement advanced data modeling, querying, and administration techniques in MongoDB * Includes rich case-studies and best practices followed by expert MongoDB developers Book Description MongoDB is the best platform for working with non-relational data and is considered to be the smartest tool for organizing data in line with business needs. The recently released MongoDB 4.x supports ACID transactions and makes the technology an asset for enterprises across the IT and fintech sectors. This book provides expertise in advanced and niche areas of managing databases (such as modeling and querying databases) along with various administration techniques in MongoDB, thereby helping you become a successful MongoDB expert. The book helps you understand how the newly added capabilities function with the help of some interesting examples and large datasets. You will dive deeper into niche areas such as high-performance configurations, optimizing SQL statements, configuring large-scale sharded clusters, and many more. You will also master best practices in overcoming database failover, and master recovery and backup procedures for database security. By the end of the book, you will have gained a practical understanding of administering database applications both on premises and on the cloud; you will also be able to scale database applications across all servers. What you will learn * Perform advanced querying techniques such as indexing and expressions * Configure, monitor, and maintain a highly scalable MongoDB environment * Master replication and data sharding to optimize read/write performance * Administer MongoDB-based applications on premises or on the cloud * Integrate MongoDB with big data sources to process huge amounts of data * Deploy MongoDB on Kubernetes containers * Use MongoDB in IoT, mobile, and serverless environments Who this book is for This book is ideal for MongoDB developers and database administrators who wish to become successful MongoDB experts and build scalable and fault-tolerant applications using MongoDB. It will also be useful for database professionals who wish to become certified MongoDB professionals. Some understanding of MongoDB and basic database concepts is required to get the most out of this book.

A practical guide for developing end-to-end serverless microservices in Python for developers, DevOps, and architects. Key Features * Create a secure, cost-effective, and scalable serverless data API * Use identity management and authentication for a user-specific and secure web application * Go beyond traditional web hosting to explore the full range of cloud hosting options Book Description Over the last few years, there has been a massive shift from monolithic architecture to microservices, thanks to their small and independent deployments that allow increased flexibility and agile delivery. Traditionally, virtual machines and containers were the principal mediums for deploying microservices, but they involved a lot of operational effort, configuration, and maintenance. More recently, serverless computing has gained popularity due to its built-in autoscaling abilities, reduced operational costs, and increased productivity. Building Serverless Microservices in Python begins by introducing you to serverless microservice structures. You will then learn how to create your first serverless data API and test your microservice. Moving on, you'll delve into data management and work with serverless patterns. Finally, the book introduces you to the importance of securing microservices. By the end of the book, you will have gained the skills you need to combine microservices with serverless computing, making their deployment much easier thanks to the cloud provider managing the servers and capacity planning. What you will learn * Discover what microservices offer above and beyond other architectures * Create a serverless application with AWS * Gain secure access to data and resources * Run tests on your configuration and code * Create a highly available serverless microservice data API * Build, deploy, and run your serverless configuration and code Who this book is for If you are a developer with basic knowledge of Python and want to learn how to build, test, deploy, and secure microservices, then this book is for you. No prior knowledge of building microservices is required.

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.

Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality. Key Features * Develop your computer vision skills by mastering algorithms in Open Source Computer Vision 4 (OpenCV 4)and Python * Apply machine learning and deep learning techniques with TensorFlow, Keras, and PyTorch * Discover the modern design patterns you should avoid when developing efficient computer vision applications Book Description OpenCV is considered to be one of the best open source computer vision and machine learning software libraries. It helps developers build complete projects in relation to image processing, motion detection, or image segmentation, among many others. OpenCV for Python enables you to run computer vision algorithms smoothly in real time, combining the best of the OpenCV C++ API and the Python language. In this book, you'll get started by setting up OpenCV and delving into the key concepts of computer vision. You'll then proceed to study more advanced concepts and discover the full potential of OpenCV. The book will also introduce you to the creation of advanced applications using Python and OpenCV, enabling you to develop applications that include facial recognition, target tracking, or augmented reality. Next, you'll learn machine learning techniques and concepts, understand how to apply them in real-world examples, and also explore their benefits, including real-time data production and faster data processing. You'll also discover how to translate the functionality provided by OpenCV into optimized application code projects using Python bindings. Toward the concluding chapters, you'll explore the application of artificial intelligence and deep learning techniques using the popular Python libraries TensorFlow, and Keras. By the end of this book, you'll be able to develop advanced computer vision applications to meet your customers' demands. What you will learn * Handle files and images, and explore various image processing techniques * Explore image transformations, including translation, resizing, and cropping * Gain insights into building histograms * Brush up on contour detection, filtering, and drawing * Work with Augmented Reality to build marker-based and markerless applications * Work with the main machine learning algorithms in OpenCV * Explore the deep learning Python libraries and OpenCV deep learning capabilities * Create computer vision and deep learning web applications Who this book is for This book is designed for computer vision developers, engineers, and researchers who want to develop modern computer vision applications. Basic experience of OpenCV and Python programming is a must.

Leverage Docker to unlock efficient and rapid container deployments to improve your development workflow Key Features * Reconfigure Docker hosts to create a logging system with the ElasticSearch-Logstash-Kibana (ELK) stack * Tackle the challenges of large-scale container deployment with this fast-paced guide * Benchmark the performance of your Docker containers using Apache JMeter Book Description Docker is an enterprise-grade container platform that allows you to build and deploy your apps. Its portable format lets you run your code right from your desktop workstations to popular cloud computing providers. This comprehensive guide will improve your Docker work?ows and ensure your application's production environment runs smoothly. This book starts with a refresher on setting up and running Docker and details the basic setup for creating a Docker Swarm cluster. You will then learn how to automate this cluster by using Chef Server and Cookbook. After that, you will run the Docker monitoring system with Prometheus and Grafana, and deploy the ELK stack. You will also learn some tips for optimizing Docker images. After deploying containers with the help of Jenkins, you will then move on to a tutorial on using Apache JMeter to analyze your application's performance. You will learn how to use Docker Swarm and NGINX to load-balance your application and how common debugging tools in Linux can be used to troubleshoot Docker containers. By the end of this book, you will be able to integrate all the optimizations that you have learned and put everything into practice in your applications. What you will learn * Automate provisioning and setting up nodes in a Docker Swarm cluster * Configure a monitoring system with Prometheus and Grafana * Use Apache JMeter to create workloads for benchmarking the performance of Docker containers * Understand how to load-balance an application with Docker Swarm and Nginx * Deploy strace, tcdump, blktrace, and other Linux debugging tools to troubleshoot containers * Integrate Docker optimizations for DevOps, Site Reliability Engineering, CI, and CD Who this book is for If you are a software developer with a good understanding of managing Docker services and the Linux file system and are looking for ways to optimize working with Docker containers, then this is the book for you. Developers fascinated with containers and workflow automation with benefit from this book.

Teach your machine to think for itself! Key Features * Delve into supervised learning and grasp how a machine learns from data * Implement popular machine learning algorithms from scratch, developing a deep understanding along the way * Explore some of the most popular scientific and mathematical libraries in the Python language Book Description Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems. What you will learn * Crack how a machine learns a concept and generalize its understanding to new data * Uncover the fundamental differences between parametric and non-parametric models * Implement and grok several well-known supervised learning algorithms from scratch * Work with models in domains such as ecommerce and marketing * Expand your expertise and use various algorithms such as regression, decision trees, and clustering * Build your own models capable of making predictions * Delve into the most popular approaches in deep learning such as transfer learning and neural networks Who this book is for This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are expected.

Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data Key Features * Learn how to select the most suitable Python library to solve your problem * Compare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use them * Delve into the applications of neural networks using real-world datasets Book Description Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you on the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The course begins by explaining how basic clustering works to find similar data points in a set. Once you are well versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. You will complete the course by challenging yourself through various interesting activities such as performing a Market Basket Analysis and identifying relationships between different merchandises. By the end of this course, you will have the skills you need to confidently build your own models using Python. What you will learn * Understand the basics and importance of clustering * Build k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages * Explore dimensionality reduction and its applications * Use scikit-learn (sklearn) to implement and analyse principal component analysis (PCA)on the Iris dataset * Employ Keras to build autoencoder models for the CIFAR-10 dataset * Apply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data Who this book is for This course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial.

Delve into the world of Kotlin and learn to build powerful Android and web applications Key Features * Learn the fundamentals of Kotlin to write high-quality code * Test and debug your applications with the different unit testing frameworks in Kotlin * Explore Kotlin's interesting features such as null safety, reflection, and annotations Book Description Kotlin is a general-purpose programming language used for developing cross-platform applications. Complete with a comprehensive introduction and projects covering the full set of Kotlin programming features, this book will take you through the fundamentals of Kotlin and get you up to speed in no time. Learn Kotlin Programming covers the installation, tools, and how to write basic programs in Kotlin. You'll learn how to implement object-oriented programming in Kotlin and easily reuse your program or parts of it. The book explains DSL construction, serialization, null safety aspects, and type parameterization to help you build robust apps. You'll learn how to destructure expressions and write your own. You'll then get to grips with building scalable apps by exploring advanced topics such as testing, concurrency, microservices, coroutines, and Kotlin DSL builders. Furthermore, you'll be introduced to the kotlinx.serialization framework, which is used to persist objects in JSON, Protobuf, and other formats. By the end of this book, you'll be well versed with all the new features in Kotlin and will be able to build robust applications skillfully. What you will learn * Explore the latest Kotlin features in order to write structured and readable object-oriented code * Get to grips with using lambdas and higher-order functions * Write unit tests and integrate Kotlin with Java code * Create real-world apps in Kotlin in the microservices style * Use Kotlin extensions with the Java collections library * Uncover destructuring expressions and find out how to write your own * Understand how Java-nullable code can be integrated with Kotlin features Who this book is for If you’re a beginner or intermediate programmer who wants to learn Kotlin to build applications, this book is for you. You’ll also find this book useful if you’re a Java developer interested in switching to Kotlin.

Take your SAP ABAP skills to the next level by mastering ABAP programming techniques with the help of real-world examples Key Features * Become adept at building interfaces and explore ABAP tools and techniques * Discover the modern functionalities available in the latest version of ABAP * Learn the process of creating stunning HTML5 applications using SAPUI5 Book Description Advanced Business Application Programming (ABAP) is an established and complex programming language in the IT industry. This book is designed to help you use the latest ABAP techniques and apply legacy constructions using practical examples. You'll start with a quick refresher on language and database concepts, followed by agile techniques for adding custom code to a modern ABAP system. After this, you will get up to speed with the complete ABAP toolset for importing data to and from different environments. Next, you'll learn how to print forms and work with the different ABAP tools for Extensible Markup Language (XML) manipulation. While covering further chapters, you'll gain insights into building stunning UI5 interfaces, in addition to learning how to develop simple apps using the Business Object Processing Framework (BOPF). You will also pick up the technique of handling exceptions and performing testing in ABAP. In the concluding chapters, you can look forward to grasping various techniques for optimizing the performance of programs using a variety of performance analysis tools. By the end of this book, you will have the expertise you need to confidently build maintainable programs in Systems, Applications, and Products (SAP). What you will learn * Create stable and error-free ABAP programs * Leverage new ABAP concepts including object-oriented programming(OOP) and Model-View-Controller (MVC) * Learn to add custom code to your existing SAP program * Speed up your ABAP programs by spotting bottlenecks * Understand techniques such as performance tuning and optimization * Develop modern and beautiful user interfaces (UIs) in an ABAP environment * Build multiple classes with any nesting level Who this book is for This book is for developers who want to learn and use ABAP skills to become an industry expert. Familiarity with object-oriented programming concepts is expected.