A hands-on guide to making system programming with C++ easy Key Features *Write system-level code leveraging C++17 *Learn the internals of the Linux Application Binary Interface (ABI) and apply it to system programming *Explore C++ concurrency to take advantage of server-level constructs Book Description C++ is a general-purpose programming language with a bias toward system programming as it provides ready access to hardware-level resources, efficient compilation, and a versatile approach to higher-level abstractions. This book will help you understand the benefits of system programming with C++17. You will gain a firm understanding of various C, C++, and POSIX standards, as well as their respective system types for both C++ and POSIX. After a brief refresher on C++, Resource Acquisition Is Initialization (RAII), and the new C++ Guideline Support Library (GSL), you will learn to program Linux and Unix systems along with process management. As you progress through the chapters, you will become acquainted with C++'s support for IO. You will then study various memory management methods, including a chapter on allocators and how they benefit system programming. You will also explore how to program file input and output and learn about POSIX sockets. This book will help you get to grips with safely setting up a UDP and TCP server/client. Finally, you will be guided through Unix time interfaces, multithreading, and error handling with C++ exceptions. By the end of this book, you will be comfortable with using C++ to program high-quality systems. What you will learn *Understand the benefits of using C++ for system programming *Program Linux/Unix systems using C++ *Discover the advantages of Resource Acquisition Is Initialization (RAII) *Program both console and file input and output *Uncover the POSIX socket APIs and understand how to program them *Explore advanced system programming topics, such as C++ allocators *Use POSIX and C++ threads to program concurrent systems *Grasp how C++ can be used to create performant system applications Who this book is for If you are a fresh developer with intermediate knowledge of C++ but little or no knowledge of Unix and Linux system programming, this book will help you learn system programming with C++ in a practical way.

Learn quick and effective techniques to get up and running with building blockchain including Ethereum and Hyperledger Fabric. Key Features *Understand the key concepts of decentralized applications and consensus algorithms *Learn key concepts of Ethereum and Solidity programming *Practical guide to get started with build efficient Blockchain applications with Ethereum and Hyperledger Book Description Blockchain is a technology that powers the development of decentralized applications.This technology allows the construction of a network with no single control that enables participants to make contributions to and receive benefits from the network directly. This book will give you a thorough overview of blockchain and explain how a blockchain works.You will begin by going through various blockchain consensus mechanisms and cryptographic hash functions. You will then learn the fundamentals of programming in Solidity – the defacto language for developing decentralize, applications in Ethereum. After that, you will set up an Ethereum development environment and develop, package, build, and test campaign-decentralized applications.The book also shows you how to set up Hyperledger composer tools, analyze business scenarios, design business models, and write a chain code. Finally, you will get a glimpse of how blockchain is actually used in different real-world domains. By the end of this guide, you will be comfortable working with basic blockchain frameworks, and develop secure, decentralized applications in a hassle-free manner. What you will learn *Understand how blockchain hashing works *Write and test a smart contract using Solidity *Develop and test a decentralized application *Build and test your application using Hyperledger Fabric *Implement business network using Hyperledger Composer *Test and interact with business network applications Who this book is for The book is for developers, analysts, or anyone looking to learn about Blockchain in a quick and easy manner.

Identify tools and techniques to secure and perform a penetration test on an AWS infrastructure using Kali Linux Key Features * Efficiently perform penetration testing techniques on your public cloud instances * Learn not only to cover loopholes but also to automate security monitoring and alerting within your cloud-based deployment pipelines * A step-by-step guide that will help you leverage the most widely used security platform to secure your AWS Cloud environment Book Description The cloud is taking over the IT industry. Any organization housing a large amount of data or a large infrastructure has started moving cloud-ward — and AWS rules the roost when it comes to cloud service providers, with its closest competitor having less than half of its market share. This highlights the importance of security on the cloud, especially on AWS. While a lot has been said (and written) about how cloud environments can be secured, performing external security assessments in the form of pentests on AWS is still seen as a dark art. This book aims to help pentesters as well as seasoned system administrators with a hands-on approach to pentesting the various cloud services provided by Amazon through AWS using Kali Linux. To make things easier for novice pentesters, the book focuses on building a practice lab and refining penetration testing with Kali Linux on the cloud. This is helpful not only for beginners but also for pentesters who want to set up a pentesting environment in their private cloud, using Kali Linux to perform a white-box assessment of their own cloud resources. Besides this, there is a lot of in-depth coverage of the large variety of AWS services that are often overlooked during a pentest — from serverless infrastructure to automated deployment pipelines. By the end of this book, you will be able to identify possible vulnerable areas efficiently and secure your AWS cloud environment. What you will learn * Familiarize yourself with and pentest the most common external-facing AWS services * Audit your own infrastructure and identify flaws, weaknesses, and loopholes * Demonstrate the process of lateral and vertical movement through a partially compromised AWS account * Maintain stealth and persistence within a compromised AWS account * Master a hands-on approach to pentesting * Discover a number of automated tools to ease the process of continuously assessing and improving the security stance of an AWS infrastructure Who this book is for If you are a security analyst or a penetration tester and are interested in exploiting Cloud environments to reveal vulnerable areas and secure them, then this book is for you. A basic understanding of penetration testing, cloud computing, and its security concepts is mandatory.

Take your financial skills to the next level by mastering cutting-edge mathematical and statistical financial applications Key Features * Explore advanced financial models used by the industry and ways of solving them using Python * Build state-of-the-art infrastructure for modeling, visualization, trading, and more * Empower your financial applications by applying machine learning and deep learning Book Description The second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples. You will start by setting up your Jupyter notebook to implement the tasks throughout the book. You will learn to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and sklearn. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. With these foundations, you will learn to apply statistical analysis to time series data, and understand how time series data is useful for implementing an event-driven backtesting system and for working with high-frequency data in building an algorithmic trading platform. Finally, you will explore machine learning and deep learning techniques that are applied in finance. By the end of this book, you will be able to apply Python to different paradigms in the financial industry and perform efficient data analysis. What you will learn * Solve linear and nonlinear models representing various financial problems * Perform principal component analysis on the DOW index and its components * Analyze, predict, and forecast stationary and non-stationary time series processes * Create an event-driven backtesting tool and measure your strategies * Build a high-frequency algorithmic trading platform with Python * Replicate the CBOT VIX index with SPX options for studying VIX-based strategies * Perform regression-based and classification-based machine learning tasks for prediction * Use TensorFlow and Keras in deep learning neural network architecture Who this book is for If you are a financial or data analyst or a software developer in the financial industry who is interested in using advanced Python techniques for quantitative methods in finance, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications by using smart machine learning techniques. Prior experience in Python is required.

A quick start guide to visualize your Elasticsearch data Key Features * Your hands-on guide to visualizing the Elasticsearch data as well as navigating the Elastic stack * Work with different Kibana plugins and create effective machine learning jobs using Kibana * Build effective dashboards and reports without any hassle Book Description The Elastic Stack is growing rapidly and, day by day, additional tools are being added to make it more effective. This book endeavors to explain all the important aspects of Kibana, which is essential for utilizing its full potential. This book covers the core concepts of Kibana, with chapters set out in a coherent manner so that readers can advance their learning in a step-by-step manner. The focus is on a practical approach, thereby enabling the reader to apply those examples in real time for a better understanding of the concepts and to provide them with the correct skills in relation to the tool. With its succinct explanations, it is quite easy for a reader to use this book as a reference guide for learning basic to advanced implementations of Kibana. The practical examples, such as the creation of Kibana dashboards from CSV data, application RDBMS data, system metrics data, log file data, APM agents, and search results, can provide readers with a number of different drop-off points from where they can fetch any type of data into Kibana for the purpose of analysis or dashboarding. What you will learn * Explore how Logstash is configured to fetch CSV data * Understand how to create index patterns in Kibana * Become familiar with how to apply filters on data * Discover how to create ML jobs * Explore how to analyze APM data from APM agents * Get to grips with how to save, share, inspect, and edit visualizations * Understand how to find an anomaly in data Who this book is for Kibana 7 Quick Start Guide is for developers new to Kibana who want to learn the fundamentals of using the tool for visualization, as well as existing Elastic developers.

Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. Key Features * Train your own models for effective prediction, using high-level Keras API * Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks * Get acquainted with some new practices introduced in TensorFlow 2.0 Alpha Book Description TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks. After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains. By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques. What you will learn * Use tf.Keras for fast prototyping, building, and training deep learning neural network models * Easily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible files * Use TensorFlow to tackle traditional supervised and unsupervised machine learning applications * Understand image recognition techniques using TensorFlow * Perform neural style transfer for image hybridization using a neural network * Code a recurrent neural network in TensorFlow to perform text-style generation Who this book is for Data scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will find this book useful. Some Python programming experience with version 3.6 or later, along with a familiarity with Jupyter notebooks will be an added advantage. Exposure to machine learning and neural network techniques would also be helpful.

Learn dynamic programming with Julia to build apps for data analysis, visualization, machine learning, and the web Key Features * Leverage Julia's high speed and efficiency to build fast, efficient applications * Perform supervised and unsupervised machine learning and time series analysis * Tackle problems concurrently and in a distributed environment Book Description Julia offers the high productivity and ease of use of Python and R with the lightning-fast speed of C++. There’s never been a better time to learn this language, thanks to its large-scale adoption across a wide range of domains, including fintech, biotech and artificial intelligence (AI). You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. This Learning Path walks you through two important collection types: arrays and matrices. You’ll be taken through how type conversions and promotions work, and in further chapters you'll study how Julia interacts with operating systems and other languages. You’ll also learn about the use of macros, what makes Julia suitable for numerical and scientific computing, and how to run external programs. Once you have grasped the basics, this Learning Path goes on to how to analyze the Iris dataset using DataFrames. While building a web scraper and a web app, you’ll explore the use of functions, methods, and multiple dispatches. In the final chapters, you'll delve into machine learning, where you'll build a book recommender system. By the end of this Learning Path, you’ll be well versed with Julia and have the skills you need to leverage its high speed and efficiency for your applications. This Learning Path includes content from the following Packt products: * Julia 1.0 Programming - Second Edition by Ivo Balbaert * Julia Programming Projects by Adrian Salceanu What you will learn * Create your own types to extend the built-in type system * Visualize your data in Julia with plotting packages * Explore the use of built-in macros for testing and debugging * Integrate Julia with other languages such as C, Python, and MATLAB * Analyze and manipulate datasets using Julia and DataFrames * Develop and run a web app using Julia and the HTTP package * Build a recommendation system using supervised machine learning Who this book is for If you are a statistician or data scientist who wants a quick course in the Julia programming language while building big data applications, this Learning Path is for you. Basic knowledge of mathematics and programming is a must.

Step through loading GIS data, creating GIS data, styling GIS and making maps with QGIS following a simple narrative that will allow you to build confidence as you progress. Key Features * Work with GIS data, a step by step guide from creation to making a map * Perform geoprocessing tasks and automate them using model builder * Explore a range of features in QGIS 3.4, discover the power behind open source desktop GIS Book Description QGIS is a user friendly, open source geographic information system (GIS). The popularity of open source GIS and QGIS, in particular, has been growing rapidly over the last few years. This book is designed to help beginners learn about all the tools required to use QGIS 3.4. This book will provide you with clear, step-by-step instructions to help you apply your GIS knowledge to QGIS. You begin with an overview of QGIS 3.4 and its installation. You will learn how to load existing spatial data and create vector data from scratch. You will then be creating styles and labels for maps. The final two chapters demonstrate the Processing toolbox and include a brief investigation on how to extend QGIS. Throughout this book, we will be using the GeoPackage format, and we will also discuss how QGIS can support many different types of data. Finally, you will learn where to get help and how to become engaged with the GIS community. What you will learn * Use existing data to interact with the canvas via zoom/pan/selection * Create vector data and a GeoPackage and build a simple project around it * Style data, both vector and raster data, using the Layer Styling Panel * Design, label, save, and export maps using the data you have created * Analyze spatial queries using the Processing toolbox * Expand QGIS with the help of plugins, model builder, and the command line Who this book is for If you know the basic functions and processes of GIS, and want to learn to use QGIS to analyze geospatial data and create rich mapping applications, then this is the book for you.

Become proficient in designing, developing and deploying effective software systems using the advanced constructs of Rust Key Features * Improve your productivity using the latest version of Rust and write simpler and easier code * Understand Rust’s immutability and ownership principle, expressive type system, safe concurrency * Deep dive into the new doamins of Rust like WebAssembly, Networking and Command line tools Book Description Rust is an empowering language that provides a rare combination of safety, speed, and zero-cost abstractions. Mastering Rust – Second Edition is filled with clear and simple explanations of the language features along with real-world examples, showing you how you can build robust, scalable, and reliable programs. This second edition of the book improves upon the previous one and touches on all aspects that make Rust a great language. We have included the features from latest Rust 2018 edition such as the new module system, the smarter compiler, helpful error messages, and the stable procedural macros. You’ll learn how Rust can be used for systems programming, network programming, and even on the web. You’ll also learn techniques such as writing memory-safe code, building idiomatic Rust libraries, writing efficient asynchronous networking code, and advanced macros. The book contains a mix of theory and hands-on tasks so you acquire the skills as well as the knowledge, and it also provides exercises to hammer the concepts in. After reading this book, you will be able to implement Rust for your enterprise projects, write better tests and documentation, design for performance, and write idiomatic Rust code. What you will learn * Write generic and type-safe code by using Rust’s powerful type system * How memory safety works without garbage collection * Know the different strategies in error handling and when to use them * Learn how to use concurrency primitives such as threads and channels * Use advanced macros to reduce boilerplate code * Create efficient web applications with the Actix-web framework * Use Diesel for type-safe database interactions in your web application Who this book is for The book is aimed at beginner and intermediate programmers who already have familiarity with any imperative language and have only heard of Rust as a new language. If you are a developer who wants to write robust, efficient and maintainable software systems and want to become proficient with Rust, this book is for you. It starts by giving a whirlwind tour of the important concepts of Rust and covers advanced features of the language in subsequent chapters using code examples that readers will find useful to advance their knowledge.

A comprehensive guide to programming with network sockets, implementing Internet protocols, designing IoT devices, and much more with C Key Features * Leverage your C or C++ programming skills to build powerful network applications * Get to grips with a variety of network protocols that allow you to load web pages, send emails, and do much more * Write portable network code for operating systems such as Windows, Linux, and macOS Book Description Network programming, a challenging topic in C, is made easy to understand with a careful exposition of socket programming APIs. This book gets you started with modern network programming in C and the right use of relevant operating system APIs. This book covers core concepts, such as hostname resolution with DNS, that are crucial to the functioning of the modern web. You’ll delve into the fundamental network protocols, TCP and UDP. Essential techniques for networking paradigms such as client-server and peer-to-peer models are explained with the help of practical examples. You’ll also study HTTP and HTTPS (the protocols responsible for web pages) from both the client and server perspective. To keep up with current trends, you’ll apply the concepts covered in this book to gain insights into web programming for IoT. You’ll even get to grips with network monitoring and implementing security best practices. By the end of this book, you’ll have experience of working with client-server applications, and be able to implement new network programs in C. The code in this book is compatible with the older C99 version as well as the latest C18 and C++17 standards. Special consideration is given to writing robust, reliable, and secure code that is portable across operating systems, including Winsock sockets for Windows and POSIX sockets for Linux and macOS. What you will learn * Uncover cross-platform socket programming APIs * Implement techniques for supporting IPv4 and IPv6 * Understand how TCP and UDP connections work over IP * Discover how hostname resolution and DNS work * Interface with web APIs using HTTP and HTTPS * Acquire hands-on experience with Simple Mail Transfer Protocol (SMTP) * Apply network programming to the Internet of Things (IoT) Who this book is for If you're a developer or a system administrator who wants to enter the world of network programming, this book is for you. Basic knowledge of C programming is assumed.

A hands-on guide to Android programming with Spring MVC, Spring Boot, and Spring Security Key Features * Build native Android applications with Spring for Android * Explore Reactive programming, concurrency, and multithreading paradigms for building fast and efficient applications * Write more expressive and robust code with Kotlin using its coroutines and other latest features Book Description As the new official language for Android, Kotlin is attracting new as well as existing Android developers. As most developers are still working with Java and want to switch to Kotlin, they find a combination of these two appealing. This book addresses this interest by bringing together Spring, a widely used Java SE framework for building enterprise-grade applications, and Kotlin. Learn Spring for Android Application Development will guide you in leveraging some of the powerful modules of the Spring Framework to build lightweight and robust Android apps using Kotlin. You will work with various modules, such as Spring AOP, Dependency Injection, and Inversion of Control, to develop applications with better dependency management. You’ll also explore other modules of the Spring Framework, such as Spring MVC, Spring Boot, and Spring Security. Each chapter has practice exercises at the end for you to assess your learning. By the end of the book, you will be fully equipped to develop Android applications with Spring technologies. What you will learn * Get to grips with the basics of the Spring Framework * Write web applications using the Spring Framework with Kotlin * Develop Android apps with Kotlin * Connect a RESTful web service with your app using Retrofilt * Understand JDBC, JPA, MySQL for Spring and SQLite Room for Android * Explore Spring Security fundamentals, Basic Authentication, and OAuth2 * Delve into Concurrency and Reactive programming using Kotlin * Develop testable applications with Spring and Android Who this book is for If you’re an aspiring Android developer or an existing developer who wants to learn how to use Spring to build robust Android applications in Kotlin, this book is for you. Though not necessary, basic knowledge of Spring will assist with understanding key concepts covered in this book.

Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key Features *A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ *A modern, practical and computational approach to Bayesian statistical modeling *A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises. Book Description The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to. What you will learn *Build probabilistic models using the Python library PyMC3 *Analyze probabilistic models with the help of ArviZ *Acquire the skills required to sanity check models and modify them if necessary *Understand the advantages and caveats of hierarchical models *Find out how different models can be used to answer different data analysis questions *Compare models and choose between alternative ones *Discover how different models are unified from a probabilistic perspective *Think probabilistically and benefit from the flexibility of the Bayesian framework Who this book is for If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.

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.

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features *Implement machine learning algorithms to build, train, and validate algorithmic models *Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions *Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You’ll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learn *Implement machine learning techniques to solve investment and trading problems *Leverage market, fundamental, and alternative data to research alpha factors *Design and fine-tune supervised, unsupervised, and reinforcement learning models *Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn *Integrate machine learning models into a live trading strategy on Quantopian *Evaluate strategies using reliable backtesting methodologies for time series *Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow *Work with reinforcement learning for trading strategies in the OpenAI Gym Who this book is for Hands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.

Explore a diverse set of meta-learning algorithms and techniques to enable human-like cognition for your machine learning models using various Python frameworks Key Features *Understand the foundations of meta learning algorithms *Explore practical examples to explore various one-shot learning algorithms with its applications in TensorFlow *Master state of the art meta learning algorithms like MAML, reptile, meta SGD Book Description Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster. Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning. By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models. What you will learn *Understand the basics of meta learning methods, algorithms, and types *Build voice and face recognition models using a siamese network *Learn the prototypical network along with its variants *Build relation networks and matching networks from scratch *Implement MAML and Reptile algorithms from scratch in Python *Work through imitation learning and adversarial meta learning *Explore task agnostic meta learning and deep meta learning Who this book is for Hands-On Meta Learning with Python is for machine learning enthusiasts, AI researchers, and data scientists who want to explore meta learning as an advanced approach for training machine learning models. Working knowledge of machine learning concepts and Python programming is necessary.

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.

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 .

Explore powerful Azure DevOps solutions to develop and deploy your software faster and more efficiently. Key Features * Build modern microservice-based systems with Azure architecture * Learn to deploy and manage cloud services and virtual machines * Configure clusters with Azure Service Fabric for deployment Book Description This Learning Path helps you understand microservices architecture and leverage various services of Microsoft Azure Service Fabric to build, deploy, and maintain highly scalable enterprise-grade applications. You will learn to select an appropriate Azure backend structure for your solutions and work with its toolkit and managed apps to share your solutions with its service catalog. As you progress through the Learning Path, you will study Azure Cloud Services, Azure-managed Kubernetes, and Azure Container Services deployment techniques. To apply all that you’ve understood, you will build an end-to-end Azure system in scalable, decoupled tiers for an industrial bakery with three business domains. Toward the end of this Learning Path, you will build another scalable architecture using Azure Service Bus topics to send orders between decoupled business domains with scalable worker roles processing these orders. By the end of this Learning Path, you will be comfortable in using development, deployment, and maintenance processes to build robust cloud solutions on Azure. This Learning Path includes content from the following Packt products: * Learn Microsoft Azure by Mohamed Wali * Implementing Azure Solutions - Second Edition by Florian Klaffenbach, Oliver Michalski, Markus Klein * Microservices with Azure by Namit Tanasseri and Rahul Rai What you will learn * Study various Azure Service Fabric application programming models * Create and manage a Kubernetes cluster in Azure Kubernetes Service * Use site-to-site VPN and ExpressRoute connections in your environment * Design an Azure IoT app and learn to operate it in various scenarios * Implement a hybrid Azure design using Azure Stack * Build Azure SQL databases with Code First Migrations * Integrate client applications with Web API and SignalR on Azure * Implement the Azure Active Directory (Azure AD) across the entire system Who this book is for If you are an IT system architect, network admin, or a DevOps engineer who wants to implement Azure solutions for your organization, this Learning Path is for you. Basic knowledge of the Azure Cloud platform will be beneficial.