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⏲ 6:51 👁 81.4M
NBC Sports
⏲ 13 minutes 9 seconds 👁 9.2K
NBC Sports
⏲ 21 minutes 22 seconds 👁 15.2K
BESIEGED BREAD directed by Soudade Kaadan is an Official Selection film at ÉCU 2016 in the Special Arab Section category. <br/><br/>Syria 2015. She smuggles the bread. He escaped the Syrian army. They know there is no way out.<br/><br/>Syrie 2015. Elle fait la contrebande du pain. Il est échappé de l’armée syrienne. Ils savent qu’il n’y a pas une sortie.
⏲ 0:44 👁 140K
Tour de France
⏲ 7 minutes 43 seconds 👁 12.9K
Lanterne Rouge
⏲ 7 minutes 25 seconds 👁 32K
Between Stokes’ Headingley heroics and Rishab Pant’s Gabba massacre, we forgot to appreciate Kusal Perera’s 153* against South Africa in Durban. What an innings it was! Chasing 300 on a 4th or a 5th-day pitch is the toughest task in international cricket but Kusal Perera made it seem easy that day.<br/>Perera's innings against South Africa in the first test in 2019 was nothing short of extraordinary. No one gave Sri Lanka a chance at the start of the match. They were battered and bruised by England at home and were smashed by New Zealand and Australia Down Under. But as they say 'Never say Never' in sports. They fought till the final second and found a superhuman in the form of Kusal Perera. Let me remind you, this wasn't scripted. But even the greatest of storytellers wouldn't have scripted it any better. Perera's innings was filled with aggressive stroke play and resilience, defying the South African bowling attack against all odds. <br/><br/>In the first test against South Africa in 2019, Sri Lanka was struggling at 52-3 chasing 304 when Kusal Perera stepped up to the crease with determination in his eyes.<br/>Facing a daunting target, Perera showcased incredible skill and resilience against a formidable South African bowling attack consisting of Dale Steyn, Keshav Maharaj, and Kagiso Rabada. Sri Lanka was slowly moving closer to the target at 206-5 but the 64th over changed things when they lost 2 wickets in two balls, this started a domino effect and Sri Lanka’s score became 226-9 in the 70th over. With Perera at 86 and the no. 11 batter as his non-striker, it looked like South Africa would cruise home easily but Perera shifted gears and started talking in boundaries. He did not spare any bowler, whether Steyn, Rabada, or Maharaj.<br/>Perera defied the odds with every stroke, displaying unmatched courage and determination. <br/>And when he reached his century, the crowd erupted in celebration, recognizing the sheer brilliance of his innings. The job wasn’t done yet.<br/>His teammates watched in awe as he single-handedly led Sri Lanka's charge towards victory.<br/>There was a chance of a catch in the 83rd over but it dropped short of Elgar at 3rd slip, du Plessis picked the ball and aimed at the non-striker’s end but missed the direct hit and went for 5 runs. Even luck was on Sri Lanka’s side that day. Perera continued his conquest and got Sri Lanka home 3 overs later.<br/>He played a match-winning knock of 153*, guiding Sri Lanka to a historic one-wicket victory their 2nd ever in test cricket. Their first one-run victory was also against South Africa at Colombo in 2006.<br/><br/>Kusal Perera's innings will be remembered as one of the greatest displays of skill and determination in the history of cricket.
⏲ 2:48 👁 95K
Yaqeen Institute
⏲ 1 minute 13 seconds 👁 3.2K
DU
⏲ 2 minutes 35 seconds 👁 110
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
Tour de France
⏲ 2 minutes 20 seconds 👁 24.2K
Chris Horner
⏲ 15 minutes 27 seconds 👁 1.9K
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