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On May 29, 1919, a total solar eclipse helped to prove Albert Einstein's theory of general relativity. <br/><br/>Einstein's theory suggested that gravity could bend light by curving the fabric of spacetime. To prove it, he thought it would be a great idea to look at background stars that appear very close to the sun. This isn't possible without a total solar eclipse, because the sun is way too bright. During totality, the sun is completely covered by the moon, which makes it possible to see stars that are usually invisible during the daytime. According to relativity, the stars would appear to be in slightly different locations in the sky. Einstein was planning to test his theory during a total solar eclipse on August 21, 1914, but World War I broke out less than a month before and ruined his plans. For another total solar eclipse in 1919, the British astronomer Sir Arthur Eddington organized two similar expeditions to Africa and Brazil. During totality, they mapped the positions of the stars near the sun to see if they appeared to have shifted. The difference in the stars' positions was tiny, but it was just enough to finally prove Einstein's theory.
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Welcome to Session 14 of our Open RAN series! In this session, we'll introduce supervised machine learning and its application in designing intelligent systems for Open RAN.<br/><br/><br/>Understanding Supervised Machine Learning:<br/>Supervised machine learning is a type of machine learning where the algorithm learns from labeled data. It involves training a model on a dataset that contains input-output pairs, where the input is the data and the output is the corresponding label or target variable. The algorithm learns to map inputs to outputs by finding patterns in the data. In Open RAN, supervised learning can be used for tasks such as predicting network performance based on historical data.<br/><br/>Types of Supervised Machine Learning:<br/>There are two main types of supervised machine learning: classification and regression. In classification, the algorithm learns to categorize data into predefined classes or categories. For example, it can classify network traffic into different application types (e.g., video streaming, web browsing). Regression, on the other hand, involves predicting continuous values or quantities. It is used when the output variable is a real or continuous value, such as predicting the signal strength of a network connection.<br/><br/>Binary and Multi-Class Classification:<br/>Binary classification involves categorizing data into two classes or categories. For example, it can be used to classify network traffic as either malicious or benign. Multi-class classification, on the other hand, involves categorizing data into more than two classes. It can be used to classify network traffic into multiple application types (e.g., video streaming, social media, email).<br/><br/>Regression in Machine Learning:<br/>Regression is a supervised learning technique used for predicting continuous values or quantities. It involves fitting a mathematical model to the data, which can then be used to make predictions. In Open RAN, regression can be used for tasks such as predicting network latency, throughput, or coverage based on various input variables such as network parameters, traffic patterns, and environmental conditions.<br/><br/>Subscribe to \
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