Based on the Big Nerd Ranch’s Kotlin Essentials course, this guide shows you how to work effectively with the Kotlin programming language through hands-on examples and clear explanations of key Kotlin concepts and foundational APIs. Written for Kotlin 1.2, in this book authors Josh Skeen and David Greenhalgh also introduce JetBrains’ IntelliJ IDEA development environment. Whether you are an experienced Android developer looking for modern features beyond what Java offers, or a new developer ready to learn your first programming language, the authors will guide you from first principles to advanced usage of Kotlin.

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This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including Pandas and Scikit-learn, author Chris Albon shows how to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics.

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This book presents recent research work in NoSQL, covering the following basic aspects: semantic data management, graph databases, and big data management in cloud environments. Author Olivier Pivert looks at the evolution of basic concepts such as data models, query languages, and new challenges regarding implementation issues. Topics covered also include SparQL query processing with Apache Spark, querying RDF data, and Fuzzy Preference queries to NoSQL graph databases.

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The authors of this book, Bruce Williams and Ben Wilson, are the creators of Absinthe, a GraphQL toolkit for the functional programming language Elixir. The book shows how to build your own flexible, high-performance APIs, and along the way demonstrates how Elixir makes all the difference for a high performance, fault-tolerant API. Find out how to offer a more tailored, cohesive experience to your users, aggregate data from different data sources, and improve your back end's maintainability with Absinthe's declarative approach to defining how your API works.

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The subtitle of this book is "Using Arquillian, Hoverfly, AssertJ, JUnit, Selenium, and Mockito", In it authors Alex Soto Bueno, Jason Porter and Andy Gumbrecht show how to implement unit and integration tests for microservice systems running on the JVM and how to work with a microservice environment built using Java EE, WildFly Swarm, and Docker. The book also covers live system testing using technologies like the Arquillian, Wiremock, and Mockito frameworks, along with techniques like contract testing and over-the-wire service virtualization.

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This book aims to immerse you in an OO mindset and to teach you real-world, object-oriented design techniques with simple and practical examples. Author Sandi Metz demonstrates how to build new applications that can “survive success” and repair existing applications that have become impossible to change. Each technique is illustrated with extended examples in Ruby, and this second edition of the book has been fully updated for Ruby 2.5.

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This practical book demonstrates techniques for extracting and transforming features - the numeric representations of raw data - into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.

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This is a comprehensive guide to penetration testing cloud services deployed in Microsoft Azure. The book is packed with real-world examples from author Matt Burrough's experience as a corporate penetration tester. It also includes sample scripts from pen-tests and "Defenders Tips" that explain how companies can reduce risk, and provides a clear overview of how to effectively perform security tests so that you can provide the most accurate assessments possible.

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This book takes you through a variety of design patterns and explains them with real-world examples so readers can get to grips with low-level details and concepts that show you how to write Python code.  Authors Kamon Ayeva and Sakis Kasampalis cover most of the Gang of Four (GoF) design patterns, which are used to solve everyday problems, along with reactive and functional patterns that help you build resilient, scalable, and robust applications. They also cover corrections, best practices and system architecture and design. 

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This book is for C# developers without any web development experience who want to get started and become productive quickly using ASP.NET Core 2.0 to build web applications. Author Andrew Lock starts with a crash course in .NET Core, immediately cutting the cord between ASP.NET and Windows. The book then shows how to create web applications step by step, systematically adding essential features like logins, configuration, dependency injection, and custom components.

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This book aims to teach developers to solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Author Noah Gift covers off-the-shelf machine learning products from Amazon, Google, and Microsoft, and demonstrates techniques using the Python data science ecosystem. The book contains workflows and examples to help you streamline and simplify every step, from deployment to production, and build scalable solutions.

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Getting to grips with deep learning techniques through frameworks such as Keras and Tensorflow means software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, author Douwe Osinga shows how to solve deep-learning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Examples are written in Python with code available on GitHub as a set of Python notebooks.

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