_bachelor-point-124-season-3-124-mega-version-124-ep-37-39-124-kajal-arefin-ome-124-dhruba-tv-drama-serial-preview-1.jpg "Bachelor Point | Season 3 | MEGA VERSION | EP 37- 39 | Kajal Arefin Ome | Dhruba Tv Drama Serial from bachelor39s poin session 4 Video Screenshot Preview 1")
Gallery_bachelor-point-124-season-3-124-mega-version-124-ep-37-39-124-kajal-arefin-ome-124-dhruba-tv-drama-serial-preview-3.jpg "Bachelor Point | Season 3 | MEGA VERSION | EP 37- 39 | Kajal Arefin Ome | Dhruba Tv Drama Serial from bachelor39s poin session 4 Video Screenshot Preview 3")
Gallery_bachelor-point-124-season-3-124-mega-version-124-ep-37-39-124-kajal-arefin-ome-124-dhruba-tv-drama-serial-preview-hqdef.jpg "Bachelor Point | Season 3 | MEGA VERSION | EP 37- 39 | Kajal Arefin Ome | Dhruba Tv Drama Serial from bachelor39s poin session 4 Video Screenshot Preview hqdefault")
GalleryPlay Video:
(Note: The default playback of the video is HD VERSION. If your browser is buffering the video slowly, please play the REGULAR MP4 VERSION or Open The Video below for better experience. Thank you!)
Jump To Video Parts
⏲ Duration: 55 minutes 44 seconds
👁 View: 131.5K times
Play Audio:👁 View: 131.5K times
Description:
Dhruba TV
Dhruba TV
Share with your friends:
Whatsapp | Viber | Telegram | Line | SMS
Email | Twitter | Reddit | Tumblr | Pinterest
Related Videos
_bachelor-point-124-season-4-124-episode-38-124-kajal-arefin-ome-124-dhruba-tv-drama-serial.webp "Dhruba TV")
⏲ 22 minutes 15 seconds 👁 3.1M
_bachelor-point-i-drama-serial-i-episode-39.webp "Channel Nine GEC")
_tumhari-chup-gentleman-atif-aslam-humayun-saeed-yumna-zaidi-zahid-ahmed-new-song-sufiscore.webp "Please follow and watch more Bollywood and Hollywood movies.<br/><br/>Song Credits:<br/>Drama Serial : Gentleman <br/>Producers : Samina Humayun Saeed & Sana Shahnawaz<br/>Executive producer (Theme song video) : Sharjeel at Shootex<br/>Title Theme song : ‘Tumhari chup’<br/>Singer : Atif Aslam<br/>Lyrics : Khalil Ul Rehman Qamar<br/>Music composed & Produced : Naveed Nashad<br/>Song Programming : Mujtaba Ali Choni<br/>Arranged & Key Works: Imran Akhtar<br/>Flute : Baqar Abbass<br/>Guitar : Rahil Mirza<br/>Rythem : Kashif and team<br/>Bass : Malvin<br/>Live Strings Session : Mumbai Strings<br/>Mix and Master : Darren Heelis<br/>Vocals Recording Session : Ali Mustafa studio<br/>Music Assistant : Wasif Nashad<br/>Studio facilities : NN Studios<br/>Gentleman theme song video credits<br/>Drama Serial : Gentleman<br/>Producers : Samina Humayun Saeed & Sana Shahnawaz<br/>Executive producer (Theme song video) : Sharjeel at Shootex<br/>Drama Director : Haissam Hussain<br/>Music Video Directer : Abdullah Haris<br/>Styling :Tabesh Khoja<br/>Makeup : Nabila<br/>Wardrobe Courtesy : Sana Safinaz, Rashmi Kumari, Sadiqa, Ismail Farid<br/>Song Programming : Mujtaba Ali Choni<br/>Arranged & Key Works: Imran Akhtar<br/>Flute : Baqar Abbass<br/>Guitar : Rahil Mirza<br/>Rythem : Kashif and team<br/>Bass : Malvin<br/>Live Strings Session : Mumbai Strings<br/>Mix & Master : Darren<br/>Vocals Recording Session : Ali Mustafa studio<br/>Music Assistant : Wasif Nashad<br/>Studio facilities : NN Studios<br/>Sound Design (sfx): Aravind Vijayakumar and Rhitwik Raj Pathak<br/>Color Grade: Sharp Image & Oblivion Films<br/>Edited By: Abdullah Haris<br/><br/>Welcome to Movie Troves, your ultimate destination for a treasure trove of cinematic experiences! Dive into a world where every frame tells a story, every scene brings excitement, and every movie leaves a lasting impression. Whether you're a seasoned cinephile or a casual viewer looking for some entertainment, Movie Troves has something special for you.")
Tumhari Chup - Gentleman - Atif Aslam -Humayun Saeed, Yumna Zaidi, Zahid ahmed- New Song - Sufiscore
⏲ 5:51 👁 85K
_bachelor-point-124-season-4-124-mega-version-124-ep-31-35-124-kajal-arefin-ome-124-dhruba-tv-drama-serial.webp "Dhruba TV")
⏲ 1 hour 28 minutes 56 seconds 👁 641K
_west-mids-pension-fund-advice-session.webp "West mids pension fund advice session at West Bromwich library.")
_bachelor-point-124-season-4-124-mega-version-124-ep-91-95-124-kajal-arefin-ome-124-dhruba-tv-drama-serial.webp "Dhruba TV")
⏲ 1 hour 30 minutes 28 seconds 👁 723.9K
_bachelor-point-124-season-4-124-mega-version-124-ep-51-55-124-kajal-arefin-ome-124-dhruba-tv-drama-serial.webp "Dhruba TV")
⏲ 1 hour 27 minutes 47 seconds 👁 618.4K
_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 \")
_bachelor-point-124-season-4-124-episode-38-124-kajal-arefin-ome-124-dhruba-tv-drama-serial.jpg)
_bachelor-point-i-drama-serial-i-episode-39.jpg)
_tumhari-chup-gentleman-atif-aslam-humayun-saeed-yumna-zaidi-zahid-ahmed-new-song-sufiscore.jpg)
_bachelor-point-124-season-4-124-mega-version-124-ep-31-35-124-kajal-arefin-ome-124-dhruba-tv-drama-serial.jpg)
_west-mids-pension-fund-advice-session.jpg)
_bachelor-point-124-season-4-124-mega-version-124-ep-91-95-124-kajal-arefin-ome-124-dhruba-tv-drama-serial.jpg)
_bachelor-point-124-season-4-124-mega-version-124-ep-51-55-124-kajal-arefin-ome-124-dhruba-tv-drama-serial.jpg)
_session-17-deployment-of-machine-learning-models-in-open-ran-networks.jpg)
![[+]](/images/menu.gif){kind=small}