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New Renault Captur E-Tech full hybrid 145 in Techno white Driving Video
⏲ 3:1 👁 335K
Terzic shields Maatsen from blame after error in Champions League final
⏲ 1:24 👁 90K
What are people doing at such a height!
⏲ 4:40 👁 85K
Mission accomplished: Kroos leaves as Real Madrid icon
⏲ 2:19 👁 85K
Wiegman denies France qualifier is a 'must-win' for England
⏲ 0:43 👁 80K
Session 17: Deployment of Machine Learning Models in Open RAN Networks
⏲ 4:9 👁 75K
'We are going to miss him' - Ancelotti praises outgoing Kroos
⏲ 0:36 👁 1.9M
Bernardo anticipating 'important' Kovacic-Gvardiol match-up
⏲ 0:52 👁 110K
Ancelotti wants more success at Real Madrid
⏲ 0:42 👁 2.3M
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_new-renault-captur-e-tech-full-hybrid-145-in-techno-white-driving-video.jpg "Renault is unveiling the high-performance version of Rafale: Renault Rafale E-Tech 4x4 300 hp. Channelling all the passion and expertise of the brand, the new plug-in hybrid powertrain delivers the best of both worlds, ICE and electric, with a preference for everyday electric driving. Renault Rafale E-Tech 4x4 300 hp is more than just a high-performance powertrain. A vehicle in tune with its times, it is ultra-efficient, with a range of up to 1,000 km (WLTP*). With the electric motor added to the rear axle, this brand flagship gains a permanently active 4-wheel drive set-up. Packed with the best in high-tech features and safety systems, Rafale E-Tech 4x4 300 hp also optimises traction and roadholding in all circumstances.<br/>The Atelier Alpine version pushes the boundaries of excellence still further in terms of sheer driving pleasure, with the support of the experts from the Alpine brand. With a chassis tuned by the engineers of Alpine Cars and a smart active suspension system, Renault Rafale E-Tech 4x4 300 hp Atelier Alpine is built to deliver exceptional handling and driving pleasure.")
_terzic-shields-maatsen-from-blame-after-error-in-champions-league-final.jpg "Edin Terzic believes Ian Maatsen 'has a bright future', despite his error leading to Real Madrid's second goal")
_what-are-people-doing-at-such-a-height.jpg "Your Quaries:-<br/><br/>At great heights, people engage in various activities, often driven by work, adventure, or scientific research. Construction workers on skyscrapers and bridges perform high-altitude tasks, securing steel beams, welding, and installing glass panes while adhering to stringent safety protocols. Climbers and mountaineers, driven by a sense of adventure and the challenge of conquering peaks, scale mountains like Everest, enduring harsh weather and thin air. Pilots and flight attendants operate commercial aircraft, ensuring safe travel for passengers thousands of feet above the ground. Researchers and meteorologists use high-altitude balloons, drones, and planes to gather data on atmospheric conditions, contributing to weather forecasting and climate studies. In tall observatories, astronomers peer into the cosmos, free from the interference of ground-level light pollution. Extreme sports enthusiasts participate in activities like skydiving, base jumping, and paragliding, seeking thrills and adrenaline rushes. At such heights, whether for work, research, or recreation, people rely on specialized training, equipment, and safety measures to navigate and thrive in these elevated environments.")
_mission-accomplished-kroos-leaves-as-real-madrid-icon.jpg "Toni Kroos bowed out from club football by winning his sixth Champions League title & a 22nd trophy with Madrid.")
_wiegman-denies-france-qualifier-is-a-39must-win39-for-england.jpg "Despite defeat to France on Friday, Sarina Wiegman doesn't believe the Lionesses' re-match is a 'must-win'.")
_session-17-deployment-of-machine-learning-models-in-open-ran-networks.jpg "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 \")
_39we-are-going-to-miss-him39-ancelotti-praises-outgoing-kroos.jpg "Real Madrid head coach Carlo Ancelotti praises the outgoing Toni Kroos after Los Blancos' UCL final triumph")
_bernardo-anticipating-39important39-kovacic-gvardiol-match-up.jpg "Portugal's Bernardo Silva will face Manchester City team-mates Mateo Kovacic and Josko Gvardiol in Croatia tie")
_ancelotti-wants-more-success-at-real-madrid.jpg "Not satisfied with Saturday's Champions League win with Real Madrid, Carlo Ancelotti is already wanting more.")
_new-renault-captur-e-tech-full-hybrid-145-in-techno-white-driving-video.webp "Renault is unveiling the high-performance version of Rafale: Renault Rafale E-Tech 4x4 300 hp. Channelling all the passion and expertise of the brand, the new plug-in hybrid powertrain delivers the best of both worlds, ICE and electric, with a preference for everyday electric driving. Renault Rafale E-Tech 4x4 300 hp is more than just a high-performance powertrain. A vehicle in tune with its times, it is ultra-efficient, with a range of up to 1,000 km (WLTP*). With the electric motor added to the rear axle, this brand flagship gains a permanently active 4-wheel drive set-up. Packed with the best in high-tech features and safety systems, Rafale E-Tech 4x4 300 hp also optimises traction and roadholding in all circumstances.<br/>The Atelier Alpine version pushes the boundaries of excellence still further in terms of sheer driving pleasure, with the support of the experts from the Alpine brand. With a chassis tuned by the engineers of Alpine Cars and a smart active suspension system, Renault Rafale E-Tech 4x4 300 hp Atelier Alpine is built to deliver exceptional handling and driving pleasure.")
_terzic-shields-maatsen-from-blame-after-error-in-champions-league-final.webp "Edin Terzic believes Ian Maatsen 'has a bright future', despite his error leading to Real Madrid's second goal")
_what-are-people-doing-at-such-a-height.webp "Your Quaries:-<br/><br/>At great heights, people engage in various activities, often driven by work, adventure, or scientific research. Construction workers on skyscrapers and bridges perform high-altitude tasks, securing steel beams, welding, and installing glass panes while adhering to stringent safety protocols. Climbers and mountaineers, driven by a sense of adventure and the challenge of conquering peaks, scale mountains like Everest, enduring harsh weather and thin air. Pilots and flight attendants operate commercial aircraft, ensuring safe travel for passengers thousands of feet above the ground. Researchers and meteorologists use high-altitude balloons, drones, and planes to gather data on atmospheric conditions, contributing to weather forecasting and climate studies. In tall observatories, astronomers peer into the cosmos, free from the interference of ground-level light pollution. Extreme sports enthusiasts participate in activities like skydiving, base jumping, and paragliding, seeking thrills and adrenaline rushes. At such heights, whether for work, research, or recreation, people rely on specialized training, equipment, and safety measures to navigate and thrive in these elevated environments.")
_mission-accomplished-kroos-leaves-as-real-madrid-icon.webp "Toni Kroos bowed out from club football by winning his sixth Champions League title & a 22nd trophy with Madrid."){kind=icon}
_wiegman-denies-france-qualifier-is-a-39must-win39-for-england.webp "Despite defeat to France on Friday, Sarina Wiegman doesn't believe the Lionesses' re-match is a 'must-win'.")
_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 \")
_39we-are-going-to-miss-him39-ancelotti-praises-outgoing-kroos.webp "Real Madrid head coach Carlo Ancelotti praises the outgoing Toni Kroos after Los Blancos' UCL final triumph")
_bernardo-anticipating-39important39-kovacic-gvardiol-match-up.webp "Portugal's Bernardo Silva will face Manchester City team-mates Mateo Kovacic and Josko Gvardiol in Croatia tie")
_ancelotti-wants-more-success-at-real-madrid.webp "Not satisfied with Saturday's Champions League win with Real Madrid, Carlo Ancelotti is already wanting more.")
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