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Fadhlina to meet with 11 vernacular school boards, PTA on BM science and maths classes
⏲ 1:24 👁 4.8M
Classification of Organic Compounds - Nomenclature [IB Chemistry SL HL]
⏲ 15 minutes 1 second 👁 115
Mole Applications [IB Chemistry SL HL]
⏲ 15 minutes 19 seconds 👁 130
Army Green Beret rates every 'John Wick' movie
⏲ 23:27 👁 360K
Classification of Organic Compounds - Functional Groups [IB Chemistry SL HL]
⏲ 16 minutes 17 seconds 👁 102
The Mole - [IB Chemistry SL HL]
⏲ 12 minutes 38 seconds 👁 215
Session 14: Introduction to Supervised Machine Learning in Open RAN
⏲ 4:28 👁 40K
Simple Harmonic Motion - Graphs [IB Physics SL HL]
⏲ 10 minutes 27 seconds 👁 327
The Amount of Chemical Change [IB Chemistry SL HL]
⏲ 16 minutes 36 seconds 👁 162
Army Green Beret rates every 'John Wick' movie
⏲ 23:27 👁 30K
Travelling Waves - Basic Wave Phenomena [IB Physics SL HL]
⏲ 8 minutes 42 seconds 👁 470
Simple Harmonic Motion - Introduction [IB Physics SL HL]
⏲ 13 minutes 34 seconds 👁 1.3K
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_fadhlina-to-meet-with-11-vernacular-school-boards-pta-on-bm-science-and-maths-classes.webp "Federal government spokesman Fahmi Fadzil says Education Minister Fadhlina Sidek will meet with the boards and parent-teacher associations (PTA) of 11 Chinese-medium secondary schools to find a solution to the implementation of a science and mathematics class being taught in Bahasa Malaysia.<br/><br/>Read more at https://tinyurl.com/3u9t5t89<br/><br/>WATCH MORE: https://thestartv.com/c/news<br/>SUBSCRIBE: https://cutt.ly/TheStar<br/>LIKE: https://fb.com/TheStarOnline")
_classification-of-organic-compounds-nomenclature-ib-chemistry-sl-hl.webp "Revision Village")
_mole-applications-ib-chemistry-sl-hl.webp "Revision Village")
_army-green-beret-rates-every-39john-wick39-movie.webp "Tu Lam, a former Special Forces soldier, rates the fighting techniques Keanu Reeves uses in the \")
_classification-of-organic-compounds-functional-groups-ib-chemistry-sl-hl.webp "Revision Village")
_the-mole-ib-chemistry-sl-hl.webp "Revision Village")
_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 \")
_simple-harmonic-motion-graphs-ib-physics-sl-hl.webp "Revision Village")
_the-amount-of-chemical-change-ib-chemistry-sl-hl.webp "Revision Village")
_army-green-beret-rates-every-39john-wick39-movie.webp "Tu Lam, a former Special Forces soldier, rates the fighting techniques Keanu Reeves uses in the \")
_travelling-waves-basic-wave-phenomena-ib-physics-sl-hl.webp "Revision Village")
_simple-harmonic-motion-introduction-ib-physics-sl-hl.webp "Revision Village")
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