Machine learning has come a long way from its early beginnings in artificial intelligence research to now being a crucial component of modern data analysis. With the release of machine learning server 9.4, there are now even more advanced features to take advantage of, resulting in improved data analysis.
One of the main features of the new machine learning server is its increased support for deep learning. Deep learning is a subfield of machine learning that leverages neural networks to learn and operate on input data. Deep learning models are capable of processing unstructured data such as images, video and audio, making them ideal for certain applications such as image recognition, speech recognition and natural language processing.
The machine learning server 9.4 now comes with integrated support for TensorFlow, a popular open-source library used for building and training deep learning models. With this integrated support, data scientists and analysts can create and deploy TensorFlow models directly from the server.
Another new feature of the machine learning server 9.4 is its enhanced integration with SQL Server. This integration allows for the seamless transfer of data from SQL Server to the machine learning server, making it easier to preprocess and analyze data. With this integration, data analysts can quickly and easily clean and transform their data before creating predictive models using the server’s machine learning features.
In addition to these major new features, the machine learning server 9.4 also includes improvements to existing features. For example, the R language support has been updated to version 3.5, providing data analysts with access to the latest tools and libraries in the R ecosystem. The server also includes enhancements to its support for Python, including support for the latest version 3.7.
All these new features and improvements in the machine learning server 9.4 make it a valuable tool for any data analyst or scientist looking to take advantage of the latest advances in machine learning and artificial intelligence. By leveraging these features, users can improve the accuracy and speed of their data analysis, allowing them to make more informed decisions and gain a competitive advantage in their respective industries.
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