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14 changes: 14 additions & 0 deletions images/AI.svg
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28 changes: 27 additions & 1 deletion index.html
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<!DOCTYPE html>
<html lang="en">
<head>
<title>HTML Intro</title>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta http-equiv="X-UA-Compatible" content="ie=edge" />
<link rel="stylesheet" href="style.css" />


</head>
<body>

<header>
<h1>Incremental and Decremental Support Vector Machine Learning</h1>
<nav>
<ul>
<li><a href="machine_learning.html">About Machine learning</a></li>
</ul>
</nav>
<p>Gert Cauwenberghs</p>
<p>CLSP, ECE Dept.</p>
<p>Johns Hopkins University</p>
<p>Baltimore, MD 21218</p>
<a href="mailto:ger@jhu.edu">ger@jhu.edu</a>
</header>
<section>
<h2>What is Lorem Ipsum?</h2>
<p>Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum</p>
</section>
<section>
<h2>Introduction</h2>
<p>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.</p>
<img src="https://docs.opencv.org/2.4/_images/optimal-hyperplane.png" alt="Optimal Hyperplane">
</section>
<footer>
<a href="https://en.wikipedia.org/wiki/Support_vector_machine">Go to Wikipedia to know more</a>
</footer>
</body>
</html>
44 changes: 44 additions & 0 deletions machine_learning.html
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<!DOCTYPE html>
<html lang="en">
<head>
<title>Machine Learning</title>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta http-equiv="X-UA-Compatible" content="ie=edge" />
<link rel="stylesheet" href="style.css" />

</head>
<body>
<header>
<h1>About MachineLearning</h1>
<nav>
<ul>
<li><a href="index.html">Home</a></li>
</ul>
</nav>
</header>
<div>
<h2>What is Machine Learning?</h2>
<p>Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.[1] Recently, artificial neural networks have been able to surpass many previous approaches in performance</p>
</div>
<div>
<video src="videos/WhatsApp Video 2024-05-07 at 15.53.21_4dfc8278.mp4" alt=" Soccer AI Video"></video>
</div>
<div>
<h2>Artificial intelligence</h2>
<p> As a scientific endeavor, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.[23] Probabilistic reasoning was also employed, especially in automated medical diagnosis.</p>
<img src="images/AI.svg" alt="Optimal Hyperplane">
</div>
<div>
<aside>
<h3>More About Machine Learning</h3>
<h4>Training models</h4>
<p>Typically, machine learning models require a high quantity of reliable data in order for the models to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative sample of data. Data from the training set can be as varied as a corpus of text, a collection of images, sensor data, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Trained models derived from biased or non-evaluated data can result in skewed or undesired predictions. Bias models may result in detrimental outcomes thereby furthering the negative impacts on society or objectives. Algorithmic bias is a potential result of data not being fully prepared for training. Machine learning ethics is becoming a field of study and notably be integrated within machine learning engineering teams.</p>
</aside>

</div>
<footer>
<a href="https://en.wikipedia.org/wiki/Machine_learning">Go to Wikipedia to know more</a>
</footer>
</body>
</html>
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