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A high-yield savings account is essentially the same as a traditional savings account with one key difference — high-yield savings accounts pay a higher than average APY on deposits. An APY, or annual percentage yield, is the amount of interest earned on an account in one year. The APY for high-yield savings accounts can be anywhere from 3% to over 5% — much higher than that of a traditional account. <br/><br/>Savings rates have been on the rise following a series of interest rate hikes by the Federal Reserve, as the central bank has sought to reduce inflation. The Fed opted to raise rates at the last meeting, raising the federal funds rate, a key bank lending rate, to a target range of 5.25%-5.50%. Experts believe that the Fed may raise rates again at the next meeting. Higher interest rates could potentially increase savings rates further, so many recommend locking in rates now while they're on the rise. Over time, rates will decrease as inflation cools.<br/><br/>Since the rate of return on high yield savings accounts is better than that of traditional savings accounts, you'll be able to accrue more cash over time. You can use our tool, in partnership with Bankrate, to compare savings rates today.

What Are High-Yield Savings Accounts?

⏲ 1:27 👁 26.8M

Colorado Football

Coach Prime's Leadership Retreat | Colorado Buffaloes Football

⏲ 7 minutes 11 seconds 👁 4.5K

AFL

Adelaide v Richmond Highlights | Round 12, 2024 | AFL

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Neel Kashkari, President of the Federal Reserve Bank of Minneapolis, has advocated for keeping interest rates unchanged until more data shows underlying inflation is declining. Kashkari believes cutting rates now could jeopardize the foundation of U.S. economic prosperity by undermining anchored inflation expectations. Kashkari does not have a vote on the FOMC this year but highlights the Fed's concerns about cutting rates prematurely before taming inflation. Kashkari says Fed should leave rates unchanged \

Fed's Neel Kashkari Advocates For No Interest Rate Cut Until 'We Get A Lot More Data To Convince Us' Amid Inflation Uncertainty

⏲ 0:40 👁 600K

FSU Football: Doak Talk

HUGE Recruiting Weekend For FSU Football!

⏲ 12 minutes 29 seconds 👁 132

BuffsNation Daily

Colorado Summer Workout Analysis. The Trenches Putting in Work

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The US job market cleared a key hurdle in April as the \

Is the US Job Market Rebounding? Key Jobs Metric Returns to Pre-Pandemic Levels

⏲ 0:43 👁 305K

Al Reynolds

Court of Public Opinion

⏲ 31 minutes 49 seconds 👁 30

ESPN FC

‘Not the sendoff they had planned’ REACTION to England’s loss to Iceland | ESPN FC

⏲ 20 minutes 58 seconds 👁 12.1K

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 \

Session 17: Deployment of Machine Learning Models in Open RAN Networks

⏲ 4:9 👁 75K

Sky Sports Football

Reaction as England LOSE to Iceland

⏲ 11 minutes 4 seconds 👁 22.9K

WWE on USA

The Bloodline Tribal Anointment | WWE SmackDown Highlights 06 07 24 | WWE on USA

⏲ 9 minutes 44 seconds 👁 25.5K

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