Hands-On Blockchain for Python Developers. Gain blockchain programming skills to build decentralized applications using Python

Arjuna Sky Kok

Blockchain is seen as the main technological solution that works as a public ledger for all cryptocurrency transactions. This book serves as a practical guide to developing a full-fledged decentralized application with Python to interact with the various building blocks of blockchain applications.Hands-On Blockchain for Python Developers starts by demonstrating how blockchain technology and cryptocurrency hashing works. You will understand the fundamentals and benefits of smart contracts such as censorship resistance and transaction accuracy. As you steadily progress, you'll go on to build smart contracts using Vyper, which has a similar syntax to Python. This experience will further help you unravel the other benefits of smart contracts, including reliable storage and backup, and efficiency. You'll also use web3.py to interact with smart contracts and leverage the power of both the web3.py and Populus framework to build decentralized applications that offer security and seamless integration with cryptocurrencies. As you explore later chapters, you'll learn how to create your own token on top of Ethereum and build a cryptocurrency wallet graphical user interface (GUI) that can handle Ethereum and Ethereum Request for Comments (ERC-20) tokens using the PySide2 library. This will enable users to seamlessly store, send, and receive digital money. Toward the end, you'll implement InterPlanetary File System (IPFS) technology in your decentralized application to provide a peer-to-peer filesystem that can store and expose media.By the end of this book, you'll be well-versed in blockchain programming and be able to build end-to-end decentralized applications on a range of domains using Python.

Getting Started with Haskell Data Analysis. Put your data analysis techniques to work and generate publication-ready visualizations

James Church

Every business and organization that collects data is capable of tapping into its own data to gain insights how to improve. Haskell is a purely functional and lazy programming language, well-suited to handling large data analysis problems. This book will take you through the more difficult problems of data analysis in a hands-on manner.This book will help you get up-to-speed with the basics of data analysis and approaches in the Haskell language. You'll learn about statistical computing, file formats (CSV and SQLite3), descriptive statistics, charts, and progress to more advanced concepts such as understanding the importance of normal distribution. While mathematics is a big part of data analysis, we've tried to keep this course simple and approachable so that you can apply what you learn to the real world.By the end of this book, you will have a thorough understanding of data analysis, and the different ways of analyzing data. You will have a mastery of all the tools and techniques in Haskell for effective data analysis.

Apache Kafka Quick Start Guide. Leverage Apache Kafka 2.0 to simplify real-time data processing for distributed applications

Raúl Estrada

Apache Kafka is a great open source platform for handling your real-time data pipeline to ensure high-speed filtering and pattern matching on the ?y. In this book, you will learn how to use Apache Kafka for efficient processing of distributed applications and will get familiar with solving everyday problems in fast data and processing pipelines.This book focuses on programming rather than the configuration management of Kafka clusters or DevOps. It starts off with the installation and setting up the development environment, before quickly moving on to performing fundamental messaging operations such as validation and enrichment.Here you will learn about message composition with pure Kafka API and Kafka Streams. You will look into the transformation of messages in different formats, such asext, binary, XML, JSON, and AVRO. Next, you will learn how to expose the schemas contained in Kafka with the Schema Registry. You will then learn how to work with all relevant connectors with Kafka Connect. While working with Kafka Streams, you will perform various interesting operations on streams, such as windowing, joins, and aggregations. Finally, through KSQL, you will learn how to retrieve, insert, modify, and delete data streams, and how to manipulate watermarks and windows.

Designing AWS Environments. Architect large-scale cloud infrastructures with AWS

Mitesh Soni, Wayde Gilchrist

Amazon Web Services (AWS) provides trusted,cloud-based solutions to help you meet your business needs. Running your solutions in the AWS Cloud can help you get your applications up and running faster while providing the security to meet your compliance requirements. This book begins by familiarizing you with the key capabilities to architect and host applications, websites, and services on AWS. We explain the available options for AWS free tier with virtual instances and demonstrate how you can launch and connect them. Using practical examples, you’ll be able to design and deploy networking and hosting solutions for large deployments. Finally, the book focuses on security and important elements of scalability and high availability using AWS VPC, Elastic Load Balancing, and Auto scaling. By the end of this book, you will have handson experience of working with AWS instances,VPC, Elastic Load Balancing, and Auto scalingrelated tasks on Amazon Web Services.

Java Data Science Cookbook. Explore the power of MLlib, DL4j, Weka, and more

Rushdi Shams

If you are looking to build data sciencemodels that are good for production,Java has come to the rescue. With the aidof strong libraries such as MLlib, Weka,DL4j, and more, you can efficientlyperform all the data science tasks youneed to.This unique book provides modernrecipes to solve your common andnot-so-common data science-relatedproblems. We start with recipes to helpyou obtain, clean, index, and search data.Then you will learn a variety of techniquesto analyze, learn from, and retrieveinformation from data. You will alsounderstand how to handle big data, learndeeply from data, and visualize data.Finally, you will work through uniquerecipes that solve your problems whiletaking data science to production, writingdistributed data science applications,and much more - things that will come inhandy at work.

Building Dashboards with Microsoft Dynamics GP 2016. Excel, Jet Reports, and MS Power BI with GP 2016 - Second Edition

Belinda Allen, Mark Polino

Microsoft Dynamics GP is a complete ERP solution that is extremely beneficial for small to midsize organizations in helping them grow exponentially.The book shows you in detail how to build great-looking dashboards with Microsoft Dynamics GP that enhance a company’s decision-making processes. This guide will take you from the basics of setting up and deploying to creating secure, refreshable Excel reports. Using a whole host of tools available within Microsoft Dynamics GP and Excel, this tutorial will show you how to visualize your data using simple conditional formatting techniques and easy-to-read charts, and allow you to make your data interactive with slicers. We will also cover core topics such as Business Analyzer, Microsoft SQL Reporting services reports, BI360, and more. You will find out to use Power BI, share and refresh data and dashboards in Power BI, and use Power BI Query Editor.By the end of this book, you will have all the information required to build interactive dashboards using Dynamics GP.

Keras Deep Learning Cookbook. Over 30 recipes for implementing deep neural networks in Python

Rajdeep Dua, Manpreet Singh Ghotra

Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy.The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning

Building Microservices with .NET Core 2.0. Transitioning monolithic architectures using microservices with .NET Core 2.0 using C# 7.0 - Second Edition

Gaurav Aroraa

The microservices architectural style promotes the development of complex applications as a suite of small services based on business capabilities. This book will help you identify the appropriate service boundaries within your business. We'll start by looking at what microservices are and their main characteristics. Moving forward, you will be introduced to real-life application scenarios; after assessing the current issues, we will begin the journey of transforming this application by splitting it into a suite of microservices using C# 7.0 with .NET Core 2.0. You will identify service boundaries, split the application into multiple microservices, and define service contracts. You will find out how to configure, deploy, and monitor microservices, and configure scaling to allow the application to quickly adapt to increased demand in the future.With an introduction to reactive microservices, you’ll strategically gain further value to keep your code base simple, focusing on what is more important rather than on messy asynchronous calls.