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We live in an era of Big Data where data of various types are being generated at an unprecedented pace, and this pace seems to be only accelerating astronomically. This data can be categorized broadly into transactional data, social media content (such as text, images, audio, and video), and sensor feeds from instrumented devices. But one may ask why it is important to pay any attention to it. The reason being: 'Data is valuable because of the decisions it enables.'
Learn design patterns with this tutorial on the original 23 Gang of Four design patterns, including diagrams, explanations, use cases, and examples.
This Refcard focuses on the design, deployment, service discovery, and management of Java applications on the open-source project called Docker so that you can get your Java application up and running inside a Docker-deployed Linux container. Expand your knowledge with extensive code snippets, informative diagrams, and workflow tips.
JavaScript, the client-side language of the web, can be used to write more sophisticated object-oriented code than the spec alone might suggest. This Refcard covers advanced object-oriented techniques in JavaScript, including object creation, member scoping, namespaces, inheritance, modularization, and more. Also includes a comparison of Java and JavaScript.
Scalability and Availability are mentioned so often that often it is difficult to know what they actually mean in each case. They are often interchanged and create confusion that results in poorly managed expectations and unrealistic metrics. This DZone Refcard provides you with the tools to define these terms so that your team can implement mission-critical systems with well-understood performance goals. This Refcard also covers: An Overview of Scalability and High Availability, Implementing Scalable Systems, Caching Strategies, Clustering, Redundancy and Fault Tolerance, Hot Tips, and More.
This Refcard is about the tools used in practical Data Mining for finding and describing structural patterns in data using Python. In recent years, Python has become more and more used for the development of data centric applications thanks to the support of a large scientific computing community and to the increasing number of libraries available for data analysis
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This Refcard covers common machine learning challenges, key patterns and anti-patterns, how to avoid MLOps mistakes, and strategies to detect anti-patterns.
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