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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 \")
_dj-hareudang-hareudang-nestapa-vita-alvia.webp "Gita Suara Music")
_vita-alvia-feat-rapx-goyang-dayung-official.webp "BW RECORD")
_92.webp "The AI channel is your go-to destination for exploring the fascinating world of artificial intelligence. Dive deep into the latest advancements, trends, and applications of AI, from machine learning and deep learning to natural language processing and computer vision. Join us on a journey of discovery as we unravel the mysteries behind intelligent systems, discuss ethical implications, and showcase cutting-edge research and innovations. Whether you're a seasoned AI enthusiast or just starting your journey, our channel provides valuable insights, tutorials, and discussions to keep you informed and inspired in this rapidly evolving field. Welcome to the future of intelligence!<br/><br/><br/>Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks typically requiring human intelligence. These tasks include problem-solving, understanding natural language, recognizing patterns, learning from experience, and making decisions. AI aims to create systems capable of performing complex tasks with autonomy and adaptability.<br/><br/>AI is broadly classified into two types:<br/><br/>Narrow AI (Weak AI): Designed to handle a specific task or a narrow range of tasks. Examples include virtual personal assistants (like Siri or Alexa), recommendation systems, and facial recognition software.<br/><br/>General AI (Strong AI): Hypothetical AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks, much like a human being. General AI remains largely theoretical and is a major goal for future AI research.<br/><br/>Key techniques in AI include:<br/><br/>Machine Learning (ML): A subset of AI focused on developing algorithms that allow computers to learn from and make predictions or decisions based on data.<br/>Deep Learning: A subset of ML that uses neural networks with many layers (deep neural networks) to analyze various factors of data.<br/>Natural Language Processing (NLP): Enables machines to understand and respond to human language.<br/>Computer Vision: Allows machines to interpret and make decisions based on visual inputs from the environment.")
_dj-pikir-keri-x-kroncong-protol-mashup-viral-tiktok-mengkane-2023.webp "Maman Fvndy Rmx")
_ternyata-ibunya-lebih-galak-dari-anaknya-prank-text-tante-sng.webp "GREK PROJECT")
_a-quiet-afternoon-on-the-beach-124-arabian-ambient.webp "Summer runs fast. The sultry heat becomes unbearable and then, you immediately run to the beach and between a rose-flavored hookah, you can also enjoy a red sunset over the sea. Lots of emotions and happiness.<br/><br/>L' estate corre veloce. Il caldo afoso diventa insopportabile ed allora, si corre subito in spiaggia e tra un narghilè al gusto di rose, ci si gusta anche un rosso tramonto sul mare. Tante emozioni e felicità.<br/>")
_characteristics-that-make-a-true-leader-part-7-team-and-strategic-plan.webp "Panabo Christian Community Church")
_vita-alvia-jangan-tinggalkan-124-dangdut-official.webp "Milady Record Official")
_92.webp "The AI channel is your go-to destination for exploring the fascinating world of artificial intelligence. Dive deep into the latest advancements, trends, and applications of AI, from machine learning and deep learning to natural language processing and computer vision. Join us on a journey of discovery as we unravel the mysteries behind intelligent systems, discuss ethical implications, and showcase cutting-edge research and innovations. Whether you're a seasoned AI enthusiast or just starting your journey, our channel provides valuable insights, tutorials, and discussions to keep you informed and inspired in this rapidly evolving field. Welcome to the future of intelligence!<br/><br/><br/>Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks typically requiring human intelligence. These tasks include problem-solving, understanding natural language, recognizing patterns, learning from experience, and making decisions. AI aims to create systems capable of performing complex tasks with autonomy and adaptability.<br/><br/>AI is broadly classified into two types:<br/><br/>Narrow AI (Weak AI): Designed to handle a specific task or a narrow range of tasks. Examples include virtual personal assistants (like Siri or Alexa), recommendation systems, and facial recognition software.<br/><br/>General AI (Strong AI): Hypothetical AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks, much like a human being. General AI remains largely theoretical and is a major goal for future AI research.<br/><br/>Key techniques in AI include:<br/><br/>Machine Learning (ML): A subset of AI focused on developing algorithms that allow computers to learn from and make predictions or decisions based on data.<br/>Deep Learning: A subset of ML that uses neural networks with many layers (deep neural networks) to analyze various factors of data.<br/>Natural Language Processing (NLP): Enables machines to understand and respond to human language.<br/>Computer Vision: Allows machines to interpret and make decisions based on visual inputs from the environment.")
_prank-ojol.webp "Aldy Haris")
_night-party-platinum-shorts-breakbeat-indoclubbing-nightpartyplatinum-diskotik-cewehot-dugem.webp "NIGHT PARTY PLATINUM")
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