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_session-14-introduction-to-supervised-machine-learning-in-open-ran.webp "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 \")
_fireboy-dml-vibration.webp "Fireboy DML")
_fireboy-dml-jealous-official-music-video.webp "Fireboy DML")
_condensed-milk-and-a-pan-is-all-you-need-for-this-dessert-ready-in-15-minutes.webp "Condensed milk and a pan is all you need for this dessert ready in 15 minutes. Delicious!<br/><br/>Ingredients:<br/>condensed milk - 380 g (13.4 oz)<br/>eggs - 2 pieces<br/>flour - 180 g (6.35 oz)<br/>baking powder - 10 g (0.35 oz)<br/>Cook it under the lid for 4 min on both sides <br/>sour cream - 400 g (14 oz)<br/>powdered sugar - 70 g (2.47 oz)<br/>cream hardener - 15 g (½ oz)<br/>dark chocolate - 50 g (1.76 oz)<br/>TO COLD 30 minutes<br/><br/>for the latte:<br/>honey - 25 g (0.9 oz)<br/>instant coffee - 30 g (1 oz)<br/>boiled water - 70 ml (2.3 fl oz)<br/>whipping cream - 70 g (2.47 oz)<br/>milk - 120 ml (½ cup)<br/>vanilla sugar - 9 g (0.3 oz)<br/>cinnamon - 1 g (0.04 oz)<br/>")
_sml-movie-jeffy39s-yes-day.webp "SML")
_open-mic-na-bata-binuhat-ko-ang-cute-ng-boses-niya-tinuruan-namin-maging-mabuting-kanser-sa-ml.webp "Scyte")
⏲ 27 minutes 57 seconds 👁 1.8M
_just-chocolate-and-milk-a-perfect-dessert-made-of-2-ingredients-without-baking-ready-in-5-min.webp "Just chocolate and milk! A perfect dessert made of 2 ingredients. Without baking, ready in 5 min<br/><br/>Ingredients:<br/>dark chocolate - 100 g (3.5 oz)<br/>sugar - 80 g (2.82 oz)<br/>cocoa - 50 g (1.76 oz)<br/>agar agar- 10 g (0.35 oz)<br/>milk - 350 ml (11.83 fl oz)<br/>whipping cream - 450 g (1.76 oz)<br/>IN THE FRIDGE 30 minutes<br/><br/>for the glaze:<br/>dark chocolate - 100 g (3.5 oz)<br/>hydrated gelatine - 2 g (0.07 oz)<br/>whipping cream - 30 ml (1 fl oz)<br/>IN THE MICROWAVE OVEN 1 minute<br/><br/>for the drink:<br/>lime - 2 pieces<br/>mint - 20 g (0.7 oz)<br/>sugar - 10 g (0.35 oz)<br/>carbonated water - 200 ml (6.76 fl oz)<br/>ice - 2 cubes<br/>")
⏲ 8:2 👁 10K
_se-konfime-yo-vl-2-kanno-nan-sitadl-bandi-kase-2-gwo-bank-nan-10-jou-plizy-gwo-koure-tonbe.webp "MACHANN ZEN HAÏTI")
Se Konfime! Yo vòlè 2 Kanno Nan Sitadèl - Bandi Kase 2 Gwo Bank 🇩🇴 Nan 10 Jou-Plizyè Gwo Koure Tonbe
⏲ 50 minutes 35 seconds 👁 17.6K
_picture-imperfection-the-walt-disney-family-massacre-124-dark-chapters-with-annie-elise.webp "Annie Elise x 10 to LIFE")
⏲ 1 hour 36 minutes 32 seconds 👁 11.5K
_no-one-will-guess-how-you-cooked-it-no-bake-dessert-simple-and-delicious.webp "No one will guess how you cooked it! No-bake dessert, simple and delicious!<br/><br/>Ingredients:<br/>agar agar: 14 g (0.5 oz)<br/>sugar: 60 g (2.1 oz)<br/>milk: 1 l (34 pt)<br/>tangerines: 5 pc<br/>milk: 150 ml (5.1 fl oz)<br/>dark chocolate: 130 g (4.6 oz)<br/>sugar: 40 g (1.4 oz)<br/>agar agar: 8 g (0.3 oz)<br/>cocoa: 25 g (0.9 oz)<br/><br/>The drink:<br/>Nutella: 25 g (0.9 oz)<br/>coffee: 100 ml (3.4 fl oz)<br/>milk: 300 ml (10 fl oz)")
_the-secret-to-solving-problems-pastor-chris-oyakhilome-ph-d.webp "Pastor Chris Oyakhilome")
_.webp "El LaithyUSA")
⏲ 6 minutes 10 seconds 👁 4.5K
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