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What are the biggest storylines heading into the NBA Finals for the Boston Celtics? The Tatum vs Luka/Ant apex predator face-off? Kyrie Irving potentially returning to TD Garden? The Celtics' haven't been truly tested until they face a Western Conference team? There are so many spicy narratives heading into the final stage of the Celtics' title run.<br/><br/>Noa Dalzell of CelticsBlog and CLNS Joins us to rank all the major narratives heading into the NBA Finals. <br/><br/>Follow Noa on Twitter/X: https://twitter.com/NoaDalzellNBA<br/>Read Noa's work for CelticsBlog: https://www.celticsblog.com/authors/noa-dalzell<br/><br/>Please LIKE this video and SUBSCRIBE to the channel!<br/><br/>Check out last week's underrated plays vid: https://youtu.be/XW_3RrudGvA<br/>️Subscribe to the podcast: https://podcasts.apple.com/podcast/first-to-the-floor-a-boston-celtics-podcast/id1351038879<br/>Follow us on Instagram: https://www.instagram.com/firsttothefloor18/<br/>Watch live Celtics games with us: https://playback.tv/celticsblog<br/>Check out Spooney's latest column on CelticsBlog: https://bit.ly/3UCITHv<br/><br/>JOIN OUR DISCORD SERVER: https://discord.gg/H75UWjmtya<br/>Buy our MERCH, Support the show!: https://bit.ly/fttfmerch<br/><br/>#bostonceltics<br/>#celtics<br/>#postgame<br/>#firsttothefloor<br/>#jaysontatum<br/>#jaylenbrown<br/>#nba
⏲ 56:31 👁 7.8M
MiniBeans
⏲ 1 minute 12 seconds 👁 3.8K
NetworkChuck Academy
⏲ 25 seconds 👁 9.6K
Dell Technologies exceeded analysts' expectations for fiscal first-quarter revenue and earnings and raised its full-year revenue guidance. Dell's AI-optimized server orders increased significantly but the company's gross margins declined due to competitive pricing and a higher mix of AI servers. Dell's AI server backlog is primarily made up of orders for Nvidia-based servers as the company works through supply chain issues. The backlog also includes large cloud customers. Extended trading saw Dell's stock price drop over 18% following the earnings call.
⏲ 0:38 👁 1.2M
PowerCert Animated Videos
⏲ 7 minutes 11 seconds 👁 317.7K
skeepy
⏲ 58 seconds 👁 1.8M
The Boston Celtics have closed out yet another series, which it means it's time for the First to the Floor end-of-series awards show! Jaylen Brown may have won the Larry Bird ECF MVP Trophy, but will he walk away with the coveted Jordan Crawford Series MVP Award? Who are the nominees for the esteemed Blake Griffin Vibesman Award? Dripping with unmatched prestige, these awards can make or break a legacy.<br/><br/>Please LIKE this video and SUBSCRIBE to the channel!<br/><br/>Check out last week's underrated plays vid: https://youtu.be/XW_3RrudGvA<br/>️Subscribe to the podcast: https://podcasts.apple.com/podcast/first-to-the-floor-a-boston-celtics-podcast/id1351038879<br/>Follow us on Instagram: https://www.instagram.com/firsttothefloor18/<br/>Watch live Celtics games with us: https://playback.tv/celticsblog<br/>Check out Spooney's latest column on CelticsBlog: https://bit.ly/3UCITHv<br/><br/>JOIN OUR DISCORD SERVER: https://discord.gg/H75UWjmtya<br/>Buy our MERCH, Support the show!: https://bit.ly/fttfmerch<br/><br/>#bostonceltics<br/>#celtics<br/>#postgame<br/>#firsttothefloor<br/>#jaysontatum<br/>#jaylenbrown<br/>#nba
⏲ 58:12 👁 285K
Aqua X Gaming
⏲ 6 minutes 10 seconds 👁 40
PowerCert Animated Videos
⏲ 7 minutes 14 seconds 👁 1.4M
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
Holistix
⏲ 5 minutes 5 seconds 👁 1.2M
OMGcraft - Minecraft Tips u0026 Tutorials!
⏲ 7 minutes 2 seconds 👁 1.7M
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