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Tips and advice from Katie Dawkins on how to improve your swingby practicing these drills.
⏲ 3:12 👁 1.6M
Microsoft Healthcare and Life Blog Videos
⏲ 18 minutes 9 seconds 👁 2.8K
Julio Casal
⏲ 3 hours 43 minutes 18 seconds 👁 51.1K
#MinecraftBuilds #Minecraft #Minecrafttutorial<br/>Does anyone read the description?<br/>If yes,let me know what else would you want me to build!
⏲ 8:10 👁 115K
Kno2gether
⏲ 37 minutes 48 seconds 👁 1K
WeAreNoCode - Learn No-Code!
⏲ 9 minutes 18 seconds 👁 7K
If you want to get a chunky, full-sounding guitar mix, there are a few tricks to get you there fast - and Skunk Anansie guitarist and ACM tutor Ace is here to show ’em to you.<br/><br/>In this video masterclass, filmed at London’s Metropolis studios, Ace covers techniques including chord approaches, choice of pickups and panning, all of which should get your guitars sounding massive.
⏲ 3:58 👁 245K
JB WEBDEVELOPER
⏲ 1 hour 57 minutes 18 seconds 👁 1.8K
Milan Jovanović
⏲ 11 minutes 46 seconds 👁 25.2K
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
Vladimir
⏲ 23 minutes 39 seconds 👁 26
Nick Chapsas
⏲ 10 minutes 14 seconds 👁 91.7K
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