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Alex De Minaur secured his first ever Grand Slam win against a top-5 player with victory over Daniil Medvedev.
⏲ 0:59 👁 175K
Gauff has called for the introduction of a replay system for umpires after her French Open semi-final exit.
⏲ 1:53 👁 225K
Ons Jabeur calls out both the French Open and Amazon Prime for the late scheduling of matches
⏲ 0:44 👁 150K
Gauff has called for the introduction of a replay system for umpires after her French Open semi-final exit.
⏲ 1:53 👁 130K
watch here new Pappu Bikare ki GirlfriendGulli BulliCartoongrannyshorttmkoc mummyshortscomedy, Do follow for watching next
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Iga Swiatek says comparisons to Rafael Nadal are 'cool' after reaching her third consecutive French Open final
⏲ 0:44 👁 115K
Andreeva admits she finds it hard to get over the disappointment of losing after her defeat to Jasmine Paolini
⏲ 0:54 👁 105K
Mirra Andreeva admits it's 'a dream' to be in the final four at Roland Garros after beating Aryna Sabalenka
⏲ 0:55 👁 95K
Novak Djokovic has withdrawn from this year's French Open ahead of Ruud quarter-final due to a knee injury.
⏲ 0:42 👁 860K
Welcome back to our journey through the world of Open RAN and machine learning. In this session, In this session, we'll explore the deployment of machine learning models in Open RAN networks, focusing on practical examples and deployment strategies.<br/><br/>Deployment Example:<br/>Consider a scenario where an Open RAN operator wants to optimize resource allocation by predicting network congestion. They decide to deploy a machine learning model to predict congestion based on historical traffic data and network conditions.<br/><br/>Deployment Steps:<br/><br/>1. Data Collection and Preprocessing:<br/>The operator collects historical traffic data, including throughput, latency, and user traffic patterns.<br/>They preprocess the data to remove outliers and normalize features.<br/><br/>2. Model Development:<br/>Data scientists develop a machine learning model, such as a regression model, to predict congestion based on the collected data.<br/>They use a development environment with libraries like TensorFlow or scikit-learn for model development.<br/><br/>3. Offline Model Training and Validation (Loop 1):<br/>The model is trained on historical data using algorithms like linear regression or decision trees.<br/>Validation is done using a separate dataset to ensure the model's accuracy.<br/><br/>4. Online Model Deployment and Monitoring (Loop 2):<br/>Once validated, the model is deployed in the network's edge servers or cloud infrastructure.<br/>Real-time network data, such as current traffic conditions, is fed into the model for predictions.<br/>Model performance is monitored using metrics like prediction accuracy and latency.<br/><br/>5. Closed-Loop Automation (Loop 3):<br/>The model's predictions are used by the network's orchestration and automation tools to dynamically allocate resources.<br/>For example, if congestion is predicted in a certain area, the network can allocate additional resources or reroute traffic to avoid congestion.<br/><br/>Subscribe to \
⏲ 4:9 👁 75K
Alexander Zverev produced a commanding performance against Casper Ruud to reach the French Open final
⏲ 1:0 👁 75K
watch here new Pirates of the Caribbean 6 Uncharted Waters - Teaser TrailerAustin Butler, Emma Stone.Do follow for watching next
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