Learn Luganda - Lesson 1 \"Greetings and Goodbyes\" from learn buganda Watch Video

Preview(s):

Play 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

Jump To learn luganda lesson 1 9234greetings and goodbyes9234 preview 1 Video PartsJump To learn luganda lesson 1 9234greetings and goodbyes9234 preview 3 Video PartsJump To learn luganda lesson 1 9234greetings and goodbyes9234 preview hqdefault Video Parts

⏲ Duration: 5 minutes 13 seconds
👁 View: 119.9K times
Play Audio:

Open HD Video
Open MP4 Video
Download HD Video
Download MP4 Video

Open MP3 Audio
Open WEBM Audio
Download MP3 Audio
Download WEBM Audio
Description:
Learn Luganda

Share with your friends:

Whatsapp | Viber | Telegram | Line | SMS
Email | Twitter | Reddit | Tumblr | Pinterest

Related Videos

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
Learn Luganda
⏲ 5 minutes 13 seconds 👁 119.9K
HOT NEWS UG
⏲ 36 minutes 39 seconds 👁 555
\
⏲ 2:40 👁 190K
UGANDA EYENKYA ™
⏲ 30 minutes 15 seconds 👁 1.1K
Welcome to Session 14 of our Open RAN series! In this session, we'll introduce supervised machine learning and its application in designing intelligent systems for Open RAN.<br/><br/><br/>Understanding Supervised Machine Learning:<br/>Supervised machine learning is a type of machine learning where the algorithm learns from labeled data. It involves training a model on a dataset that contains input-output pairs, where the input is the data and the output is the corresponding label or target variable. The algorithm learns to map inputs to outputs by finding patterns in the data. In Open RAN, supervised learning can be used for tasks such as predicting network performance based on historical data.<br/><br/>Types of Supervised Machine Learning:<br/>There are two main types of supervised machine learning: classification and regression. In classification, the algorithm learns to categorize data into predefined classes or categories. For example, it can classify network traffic into different application types (e.g., video streaming, web browsing). Regression, on the other hand, involves predicting continuous values or quantities. It is used when the output variable is a real or continuous value, such as predicting the signal strength of a network connection.<br/><br/>Binary and Multi-Class Classification:<br/>Binary classification involves categorizing data into two classes or categories. For example, it can be used to classify network traffic as either malicious or benign. Multi-class classification, on the other hand, involves categorizing data into more than two classes. It can be used to classify network traffic into multiple application types (e.g., video streaming, social media, email).<br/><br/>Regression in Machine Learning:<br/>Regression is a supervised learning technique used for predicting continuous values or quantities. It involves fitting a mathematical model to the data, which can then be used to make predictions. In Open RAN, regression can be used for tasks such as predicting network latency, throughput, or coverage based on various input variables such as network parameters, traffic patterns, and environmental conditions.<br/><br/>Subscribe to \
⏲ 4:28 👁 40K
Mukwano Gwabato
⏲ 1 minute 33 seconds 👁 123.5K
Fun Caboodle
⏲ 8 minutes 23 seconds 👁 1.4M

Related Video Searches

Back to Search

«Back to learn buganda Videos

Search Videos

Recent Searches

মেয়েদের বগলের চুলেদের ছবি মেয়েদের চà | gomovies com punjabi movies | gameshed online | cebgidp cwg | monre kane all song | parikhitbala | বল বল রে বল সবে বল রে বাঙালির জয় হারমনিয়াম সহ স্বরলিপি | x8zhwqe | dragon ball vs naruto one piece | x8zgmp2 | tamil baal hot usda suit video | www com ondhokarer golpo naznin akter happyvideo starmagi video downloadmi mela chute galapagos full | hindi nokia tamanna photo | vdm252782490 | new new com | www com hot বালকামা দেখাও কয়েল মল্লিক নাইকারুদা ai and ালোসিয়ান popy hot aunty সাথে চুদাচুদিমি মৌয়সুমি দেখবেন খান ও অপৠর চৠদাচৠদ | indian old bra | clermont real estate ct | w3 fpilaqoe | x8xbb6i | baby doll remix song | bangla senna sob | নেশা করিয়ে স্কুল ছাত্রীর দেহভোগ | lima come video | বড়মাং ও বড় বড়দুধেরবুনি | x902ble | rawderathor | የዘፈን | daygame cringe a psl classic itv | 1yibhfo8r g | www bangla power movie songangla movie agnee video song musicjan মেয়েদ | x8pqxgq | mp music albam | سکس فیلم بلوچ | india montana slim | youtube friends clean lyrics | اخت تربن زن دنیت | gia libh | sss property of congruence | ami pagol deewana ho | bd new bow bodol cikbuz com | obd2 codes p0456 fix | astrid gita bigo | mauli easy piano notes | tik tok song bring it around now | youtube com videos korea | ccoh | hey ma mp4 | shiha | meyeder male pora video nokia moron deho | opurbideo | x8zr6cw | s cc4lopjlm | x8zlwkq | new max pearl | aishwarya abhishek photos chainig girl bf vidio 3gp dounlod | www akon song com | lego 10717 asda | hits 2018 | صحنه های سکسی نوش آفرین | 201 mp3 | desafia dose dupra | tnega msr | nba most championships | খাওয়ার গলপ | web series hot | x8y3sp8 | x8zv252 | kingdom season 5 release date | mon aka lage mp3 by persia kris ray inc metro video | hot tamil danceangladeshi new video 201 | sopner rood allbam er jani akdin tomar song | japanese video game companies | velu prabhakaranin kadhal kadhai full movie | run raja run movie songs | sab dr behind tv ads | আমার হার কালা করলা৮মরে | bhabhi hd | গোসল | x8zxq0c | x9025wy |