Hands-On Android UI Development. Design and develop attractive user interfaces for Android applications

Jason Morris

A great user interface (UI) can spell the difference between success and failure for any new application. This book will show you not just how to code great UIs, but how to design them as well. It will take novice Android developers on a journey, showing them how to leverage the Android platform to produce stunning Android applications. Begin with the basics of creating Android applications and then move on to topics such as screen and layout design. Next, learn about techniques that will help improve performance for your application. Also, explore how to create reactive applications that are fast, animated, and guide the user toward their goals with minimal distraction. Understand Android architecture components and learn how to build your application to automatically respond to changes made by the user. Great platforms are not always enough, so this book also focuses on creating custom components, layout managers, and 2D graphics. Also, explore many tips and best practices to ease your UI development process. By the end, you'll be able to design and build not only amazing UIs, but also systems that provide the best possible user experience.

Learning Functional Programming in Go. Change the way you approach your applications using functional programming in Go

Lex Sheehan

Lex Sheehan begins slowly, using easy-to-understand illustrations and working Go code to teach core functional programming (FP) principles such as referential transparency, laziness, recursion, currying, and chaining continuations.This book is a tutorial for programmers looking to learn FP and apply it to write better code. Lex guides readers from basic techniques to advanced topics in a logical, concise, and clear progression.The book is divided into four modules. The first module explains the functional style of programming: pure functional programming, manipulating collections, and using higher-order functions. In the second module, you will learn design patterns that you can use to build FP-style applications. In the next module, you will learn FP techniques that you can use to improve your API signatures, increase performance, and build better cloud-native applications. The last module covers Category Theory, Functors, Monoids, Monads, Type classes and Generics.By the end of the book, you will be adept at building applications the FP way.

Machine Learning for OpenCV. Intelligent image processing with Python

Michael Beyeler

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind.OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for.Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning.By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!

IBM Watson Projects. Eight exciting projects that put artificial intelligence into practice for optimal business performance

James D. Miller

IBM Watson provides fast, intelligent insight in ways that the human brain simply can't match. Through eight varied projects, this book will help you explore the computing and analytical capabilities of IBM Watson.The book begins by refreshing your knowledge of IBM Watson's basic data preparation capabilities, such as adding and exploring data to prepare it for being applied to models. The projects covered in this book can be developed for different industries, including banking, healthcare, media, and security. These projects will enable you to develop an AI mindset and guide you in developing smart data-driven projects, including automating supply chains, analyzing sentiment in social media datasets, and developing personalized recommendations.By the end of this book, you'll have learned how to develop solutions for process automation, and you'll be able to make better data-driven decisions to deliver an excellent customer experience.

Blockchain Developer's Guide. Develop smart applications with Blockchain technologies - Ethereum, JavaScript, Hyperledger Fabric, and Corda

Brenn Hill, Samanyu Chopra, Paul Valencourt, Narayan Prusty

Blockchain applications provide a single-shared ledger to eliminate trust issues involving multiple stakeholders. It is the main technical innovation of Bitcoin, where it serves as the public ledger for Bitcoin transactions.Blockchain Developer's Guide takes you through the electrifying world of blockchain technology. It begins with the basic design of a blockchain and elaborates concepts, such as Initial Coin Offerings (ICOs), tokens, smart contracts, and other related terminologies. You will then explore the components of Ethereum, such as Ether tokens, transactions, and smart contracts that you need to build simple DApps.Blockchain Developer's Guide also explains why you must specifically use Solidity for Ethereum-based projects and lets you explore different blockchains with easy-to-follow examples. You will learn a wide range of concepts - beginning with cryptography in cryptocurrencies and including ether security, mining, and smart contracts. You will learn how to use web sockets and various API services for Ethereum. By the end of this Learning Path, you will be able to build efficient decentralized applications.This Learning Path includes content from the following Packt products:• Blockchain Quick Reference by Brenn Hill, Samanyu Chopra, Paul Valencourt• Building Blockchain Projects by Narayan Prusty

Hands-On Automated Machine Learning. A beginner's guide to building automated machine learning systems using AutoML and Python

Sibanjan Das, Umit Mert Cakmak

AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you’ll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you’ll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions.

Network Vulnerability Assessment. Identify security loopholes in your network’s infrastructure

Sagar Rahalkar

The tech world has been taken over by digitization to a very large extent, and so it’s become extremely important for an organization to actively design security mechanisms for their network infrastructures. Analyzing vulnerabilities can be one of the best ways to secure your network infrastructure.Network Vulnerability Assessment starts with network security assessment concepts, workflows, and architectures. Then, you will use open source tools to perform both active and passive network scanning. As you make your way through the chapters, you will use these scanning results to analyze and design a threat model for network security. In the concluding chapters, you will dig deeper into concepts such as IP network analysis, Microsoft Services, and mail services. You will also get to grips with various security best practices, which will help you build your network security mechanism.By the end of this book, you will be in a position to build a security framework fit for an organization.

Machine Learning with R Cookbook. Analyze data and build predictive models - Second Edition

AshishSingh Bhatia, Yu-Wei, Chiu (David Chiu)

Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.