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Published by Unbox.ai Preview:
Machine learning models, especially deep neural networks, are trained using large amounts of data. However, for many machine learning use cases, real-world data sets do not exist or are prohibitively costly to buy and label. In such scenarios, synthetic data represents an appealing, less expensive, and scalable solution. Additionally, several real-world machine learning problems suffer from class imbalance—that is, where the distribution of the categories of data is skewed, resulting in disproportionately fewer observations for one or more categories. Synthetic data can be used in such situations to balance out the underrepresented data and train models that generalize well in real-world settings. Synthetic data is now increasingly used for various applications, such as computer vision, image recognition, speech recognition, and time-series data, among others. In this article, you will learn about synthetic data, its benefits, and how it is generated for different use cases. 👉 Here is the full article
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Published by Earthly.dev Preview:
Bash (bourne again shell) scripts give you the ability to turn series of manual commands into an easily runnable and repeatable script. This can be especially useful when working with files. For programmers, Bash enables you to efficiently search for particular keywords or phrases by reading each line separately. Bash can also be used for reading files for a variety of reasons, like shell scripting, searching, text processing, building processes, logging data, and automating administrative tasks. When you’re done with this article, you’ll be able to use Bash to read files line by line, use custom delimiters, assign variables, and more. 👉 Here is the full article Published by Domino Data Lab Preview:
Data governance refers to the process of managing enterprise data with the aim of making data more accessible, reliable, usable, secure, and compliant across an organization. It is a critical feature of organizational data management and promotes better data quality and data democratization. A well-planned data-governance framework is fundamental for any data-driven organization that aims to harness the business value of its data and downstream capabilities that drive robust decision-making. It covers and details best practices for data processes, roles, policies, standards, and metrics. Naturally, data-governance frameworks vary from one organization to the next. Here are a few examples of strong data-governance frameworks recommended at companies like PWC, Hubspot, and ING. However, there are a set of commonly accepted best practices, as listed below:
In this article, you’ll learn more about data-governance frameworks and their essential components, exploring use cases and best practices for choosing a data-governance framework for your organization. 👉 Here is the full article |
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