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Movie Story: A usual working day turns unusual for Guru and Harini when they get trapped in their haunted office. A patterned game unlocks a mystery and a lift is their only way out.<br/><br/>

Lift (2021) UNCUT Dual Audio _Horror, Mystery

⏲ 2:14:48 👁 315K

DJ LiftOFF

How to Scratch for BEGINNERS: Chirp & Baby Scratch Pattern Breakdown (Practice for ALL DJs)

⏲ 8 minutes 20 seconds 👁 6.5K

DJ LiftOFF

4 Easy to Learn Scratch Patterns to Transition in to Songs for DJs

⏲ 10 minutes 👁 17.3K

THEO3 - HOW I FLOW part 2 W TONA and ANDREENA MILL (BOI1DA) www.Theology3.com

⏲ 2 min 47 sec ✓ 23-May-2010

DJ LiftOFF

Do THIS to Continue Becoming a Better Scratch DJ (Step-by-Step)

⏲ 11 minutes 24 seconds 👁 7.9K

WIRED

15 Levels of Turntable Scratching: Easy to Complex | WIRED

⏲ 18 minutes 45 seconds 👁 753.2K

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

DJ LiftOFF

Easy Scratch Technique to Add to Your Scratch Patterns | Step-by-Step Breakdown

⏲ 8 minutes 40 seconds 👁 3.6K

DJ LiftOFF

How to Learn New Scratch Patterns for DJs (DJ Scratch Pattern Breakdown)

⏲ 9 minutes 49 seconds 👁 2K

Knowing a Changeless God - DJ from how to make dj pattern in fl studio

Knowing a Changeless God - DJ

⏲ 28:30 👁 75K

DJ LiftOFF

How To Learn and Practice New Scratch Patterns (DJ Jazzy Jeff Scratch Pattern Breakdown)

⏲ 8 minutes 26 seconds 👁 7.1K

DJ LiftOFF

DJ Puffy Scratch Pattern for Transitions | Step-By-Step Guide

⏲ 9 minutes 22 seconds 👁 4.8K

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