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A bunch of Shropshire men are travelling the length of the country on vintage tractors, and its all for charity. We catch up with them as they pass through Shrewsbury to find out more.
⏲ 4:30 👁 770K
Jamos Parsa - Topic
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Boris - Topic
⏲ 4 minutes 3 seconds 👁 4
Vintage US Army Harley Davidson motorbikes took part in a parade near the D-Day beaches.Source: AP
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Classical Music Compilation
⏲ 7 hours 25 minutes 👁 5.1M
Vanilla Ice
⏲ 3 minutes 58 seconds 👁 276.2M
Fly about 2,600 light-years to Hubble Space Telescope imagery of the Cygnus loop nebula. See images from 2001 and 2020. <br/><br/>Credit: NASA, ESA, STScI<br/>Acknowledgments:<br/>NSF's NOIRLab, Akira Fujii , Jeff Hester , Davide De Martin , Travis A. Rector , Ravi Sankrit (STScI), DSS<br/>Edited by Space.com's Steve Spaleta
⏲ 1:13 👁 110K
Danielle Marsaili - Topic
⏲ 1 minute 11 seconds 👁 2
Just Audio
⏲ 1 hour 25 minutes 50 seconds 👁 193.5K
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 \
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ANA[DIA]LOG
⏲ 17 minutes 29 seconds 👁 7K
The Local Train
⏲ 3 minutes 54 seconds 👁 28.4M
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