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Credit: SWNS / Kimberly Di Benedetto<br/><br/>A woman who began losing her hair aged 19 launched a wig company to give people their confidence back.<br/><br/>Kimberly Di Benedetto, 34, began noticing her hair thinning in her teens and was diagnosed with female pattern hair loss.<br/><br/>Her confidence plummeted and she tried everything to get her hair to grow back - including laser therapy, vitamins, scalp injections and creams.<br/><br/>When none worked she looked into hair pieces and toppers and says they changed her life.<br/><br/>After connecting with others experiencing hair loss, Kimberly launched Lusta Hair - a brand selling realistic, natural human hair wigs and hair pieces.
⏲ 1:42 👁 7.3M
Mr. Sinn
⏲ 3 minutes 34 seconds 👁 42.2K
RED DEVIL GAMER
⏲ 14 seconds 👁 250
While international attention has been on Gaza, dozens of Palestinian communities in the occupied West Bank are being displaced. Violent attacks by Israeli settlers are on the rise, while Israel declares ever more land there as Israeli-state owned. <br/> <br/> <br/>#Palestine #WestBank #IsraeliSettlements #DisplacementCrisis #OccupiedTerritories #HumanRightsViolation #GazaConflict #PalestinianRights #LandGrab #SettlerViolence #InternationalAttention #IsraeliOccupation #DisplacedCommunities #HumanitarianCrisis #SettlementExpansion<br/>~PR.152~ED.194~HT.178~
⏲ 5:11 👁 170K
Geography with Dave
⏲ 41 minutes 43 seconds 👁 19.5K
Mr. Sinn
⏲ 2 minutes 36 seconds 👁 46.4K
Movie Story: A usual working day turns unusual for Guru and Harini when they get trapped in their haunted office. A patterned game unlocks a mystery and a lift is their only way out.<br/><br/>
⏲ 2:14:48 👁 310K
Half as Interesting
⏲ 5 minutes 54 seconds 👁 441.6K
John Lanser
⏲ 4 minutes 9 seconds 👁 125.2K
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
Versed
⏲ 9 minutes 3 seconds 👁 9K
aus navigation
⏲ 5 minutes 45 seconds 👁 64.8K
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