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Alia Bhatt, Ranbir Kapoor with Baby Raha return to mumbai from Anant Radhika 2nd Pre Wedding. Watch video to know more <br/> <br/>#AliaBhatt #RanbirKapoor #RahaKapoor #AnantRadhika2ndPreWedding <br/>~PR.132~ED.140~
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Harishchandra Shinde
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Dharmesh vlog
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The cruise pre-wedding celebrations of Anant Ambani and Radhika Merchant in Italy have been the talk of the town for quite some time now. Several pictures and videos from the celebrations have gone viral on social media. One of the inside videos showed rumoured lovebirds Janhvi Kapoor and Shikhar Pahariya. In addition to this, the long-awaited inside visuals of Shah Rukh Khan, Salman Khan, Ajay Devgn, Sidharth Malhotra, and Kiara Advani also surfaced online. After attending the lavish party, SRK, with his family, Ranveer Singh, Kareena Kapoor, Ranbir Kapoor, and Alia Bhatt, among others, have returned to Mumbai.<br/>
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GARMI
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Vulture Wave
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LUSTVILLA
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unlimited fun 4u
⏲ 21 seconds 👁 252.6K
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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UHD HUNTERYT
⏲ 31 seconds 👁 22.4K
Aliya bharti 2022
⏲ 29 seconds 👁 2M
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