_at-the-airport-shorts-viral-trending-foryou-tiktok-delicious-gaming-reelsdailymotion-france-pakistan-algeria-india-usa-i.jpg "at the airport #shorts #viral #trending #foryou #tiktok #delicious #gaming #reels#dailymotion #France #Pakistan #Algeria #India #USA #Iran #entertainment #tour #fun from tofus minecraft server Video Screenshot Preview")
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✓ Published: 03-Jun-2024
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✓ Published: 03-Jun-2024
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_aphmau-is-leaving-the-server-in-minecraft.webp "Aphmau")
_this-roblox-youtuber-should-be-banned-tofu.webp "Schlep")
_i-made-3-epic-projects-with-minecraft-challenges-and-roblox-horror.webp "Hello friends!Are you ready for an epic crafting adventure combining the best of Minecraft and Roblox? ✨ We've got three amazing projects for you today that will ignite your creativity and bring your favorite games to life! <br/><br/>First up, we took on the ultimate Minecraft Diorama Challenge! ⏳️ We created three stunning dioramas within different time limits: 10 minutes, 1 hour, and 10 hours. ⏲️️ From paper block caves with creepers to intricate Styrofoam landscapes with LED-lit gold ore, our craftsmen put their skills to the test. Watch us transform basic materials into detailed Minecraft worlds, featuring iconic characters like Steve and his trusty pig. ✨ Next, we crafted a Minecraft Diamond Ore Lamp with a secret cave inside!Using Lego blocks, construction foam, plaster, and LED lights, we built a glowing diamond ore with a hidden mini-world inside. We even added realistic details like earth blocks, artificial grass, and plasticine characters.Finally, we ventured into the spooky world of Roblox with a DIY Roblox Door Horror Game!Inspired by the popular Roblox Door game, we created a board game where you must navigate through terrifying rooms filled with monsters like \")
_i-started-a-war-on-my-school39s-minecraft-server.webp "Parrot")
_let-me-see-yo.webp "haha")
_i-survived-on-1-emerald-for-100-days.webp "Branzy")
_what-your-minecraft-skin-says-about-you.webp "MVPerry")
_session-17-deployment-of-machine-learning-models-in-open-ran-networks.webp "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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_session-17-deployment-of-machine-learning-models-in-open-ran-networks.jpg)
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