Echo2
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Beginning Google Web Toolkit
The open source, lightweight Google Web Toolkit (GWT) is a framework that allows Java developers to build rich Internet applications (RIAs), more recently called Ajax applications, in Java. Typically, writing these applications requires a lot of JavaScript development. However, Java and JavaScript are very distinctively different languages (although the name suggests otherwise), therefore requiring a different development process. In Beginning Google Web Toolkit: From Novice to Professional, you'll learn to build rich, user–friendly web applications using a popular Java–based Ajax web framework, the Google Web Toolkit. The authors will guide you through the complete development of a GWT front-end application with a no–nonsense, down–to–earth approach. You'll start with the first steps of working with GWT and learn to understand the concepts and consequences of building this kind of application. During the course of the book, all the key aspects of GWT are tackled pragmatically, as you're using them to build a real–world sample application. Unlike many other books, the inner workings of GWT and other unnecessary details are shelved, so you can focus on the stuff that really matters when developing GWT applications.
Identity Security for Software Development
Maintaining secrets, credentials, and nonhuman identities in secure ways is an important, though often overlooked, aspect of secure software development. Cloud migration and digital transformation have led to an explosion of nonhuman identities—like automation scripts, cloud native apps, and DevOps tools—that need to be secured across multiple cloud and hybrid environments. DevOps security often addresses vulnerability scanning, but it neglects broader discussions like authentication, authorization, and access control, potentially leaving the door open for breaches. That's where an identity security strategy focused on secrets management can help. In this practical book, authors John Walsh and Uzi Ailon provide conceptual frameworks, technology overviews, and practical code snippets to help DevSecOps engineers, cybersecurity engineers, security managers, and software developers address use cases across CI/CD pipelines, Kubernetes and cloud native, hybrid and multicloud, automation/RPA, IOT/OT, and more. You'll learn: The fundamentals of authentication, authorization, access control, and secrets management What developers need to know about managing secrets and identity to build safer apps What nonhuman identities, secrets, and credentials are—and how to secure them How developers work with their cross-function peers to build safer apps How identity security fits into modern software development practices
Scala:Applied Machine Learning
Author: Pascal Bugnion
language: en
Publisher: Packt Publishing Ltd
Release Date: 2017-02-23
Leverage the power of Scala and master the art of building, improving, and validating scalable machine learning and AI applications using Scala's most advanced and finest features About This Book Build functional, type-safe routines to interact with relational and NoSQL databases with the help of the tutorials and examples provided Leverage your expertise in Scala programming to create and customize your own scalable machine learning algorithms Experiment with different techniques; evaluate their benefits and limitations using real-world financial applications Get to know the best practices to incorporate new Big Data machine learning in your data-driven enterprise and gain future scalability and maintainability Who This Book Is For This Learning Path is for engineers and scientists who are familiar with Scala and want to learn how to create, validate, and apply machine learning algorithms. It will also benefit software developers with a background in Scala programming who want to apply machine learning. What You Will Learn Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters Apply key learning strategies to perform technical analysis of financial markets Understand the principles of supervised and unsupervised learning in machine learning Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet Construct reliable and robust data pipelines and manage data in a data-driven enterprise Implement scalable model monitoring and alerts with Scala In Detail This Learning Path aims to put the entire world of machine learning with Scala in front of you. Scala for Data Science, the first module in this course, is a tutorial guide that provides tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed building data science and data engineering solutions. The second course, Scala for Machine Learning guides you through the process of building AI applications with diagrams, formal mathematical notation, source code snippets, and useful tips. A review of the Akka framework and Apache Spark clusters concludes the tutorial. The next module, Mastering Scala Machine Learning, is the final step in this course. It will take your knowledge to next level and help you use the knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. By the end of this course, you will be a master at Scala machine learning and have enough expertise to be able to build complex machine learning projects using Scala. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Scala for Data Science, Pascal Bugnion Scala for Machine Learning, Patrick Nicolas Mastering Scala Machine Learning, Alex Kozlov Style and approach A tutorial with complete examples, this course will give you the tools to start building useful data engineering and data science solutions straightaway. This course provides practical examples from the field on how to correctly tackle data analysis problems, particularly for modern Big Data datasets.