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_roaring-kitty-holds-onto-massive-gamestop-position-despite-monday39s-rally.webp "Keith Gill, aka 'Roaring Kitty', seemingly held on to his large GameStop position even after a significant rally on Monday. GameStop shares surged over 20% during Monday’s session, with an intraday high of 70%. Gill posted a screenshot of his portfolio after the market closed on Monday, showing the same common stock and call option holdings as he did on Sunday evening. His holdings included 5 million shares of GameStop and 120,000 call options with a $20 strike price expiring on June 21. GameStop shares climbed about 4% in extended trading following his post. The value of his common stock stake increased from over $115 million to $140 million in one day.")
_the-super-trio-musical-performance-set-stage-on-fire-124-superstar-singer-3-124-sat-sun-8-pm.webp "SET India")
⏲ 30 seconds 👁 4K
_spotlight-ft-cassie-amp-carlotta-extended-cut-124-season-3-ep-3-124-star.webp "Star")
⏲ 2 minutes 46 seconds 👁 507.6K
_manchester-announces-scholarship-for-indian-students.webp "Caption- Secure University of Manchester's Scholarships with Admissify!Our experts will guide you through every step to secure prestigious merit-based awards for international students. From scholarship essays to interview prep, we help boost your chances. So, don't miss out and book a FREE counselling session now!. The link is in the profile.")
_i-want-you-ft-star-cast-124-season-2-ep-8-124-star.webp "Star")
_9234breathless9234-extended-music-video-124-season-3-124-star.webp "Star")
_dad-trains-toddler-in-adorable-92.webp "Get ready for a heartwarming overload of pure joy!This incredible video captures the adorable bond between a father and his toddler during a playful training session.Forget boring drills! This dad takes a creative approach, transforming playtime into a mini fighter workout.With padded mitts and lighthearted jabs, the toddler throws punches with infectious enthusiasm. <br/><br/>Video ID: WGA329226<br/><br/>All the content on Heartsome is managed by WooGlobe<br/><br/>For licensing and to use this video, please email licensing(at)Wooglobe(dot)com.<br/><br/>►SUBSCRIBE for more Heartsome Videos: <br/><br/>-----------------------<br/>Copyright - #wooglobe #heartsome <br/>#heartwarmingfathersonmoment #adorabletoddlertraining #playfuldadandson #viralvideo #preciousfatherandchildbond #fatherhood #childdevelopment #playtimeactivities #positiveparenting #cantstopwontstop #toocute #familyfun #minime #futurechampion #makingmemories #daddyslittlefighter #preciousmoments #caughtoncamera #familiesofinstagram #loveyoutothemoonandback<br/>")
_star-season-2-9234stronger-than-ever9234-promo-hd.webp "TV Promos")
_sound-house-cancels-the-tour-124-season-3-ep-1-124-star.webp "Star")
_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 \")
_everything-you-need-to-know-about-season-1-of-star-124-season-1-124-star.webp "Star")
_come-and-talk-to-me-ft-take-3-124-season-2-ep-14-124-star.webp "Star")
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