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index.html
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<!DOCTYPE html>
<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]-->
<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]-->
<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]-->
<head>
<meta charset="utf-8">
<title>Sasi Bonu</title>
<meta name="description" content="">
<meta name="viewport" content="width=device-width">
<link rel="stylesheet" href="http://fonts.googleapis.com/css?family=Roboto+Slab:400,700,300,100">
<link rel="stylesheet" href="http://fonts.googleapis.com/css?family=Roboto:400,400italic,300italic,300,500,500italic,700,900">
<!--
Artcore Template
http://www.templatemo.com/preview/templatemo_423_artcore
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<link rel="stylesheet" href="css/bootstrap.css">
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<link rel="stylesheet" href="css/templatemo-style.css">
<script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script>
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<body>
<!--[if lt IE 7]>
<p class="chromeframe">You are using an outdated browser. <a href="http://browsehappy.com/">Upgrade your browser today</a> or <a href="http://www.google.com/chromeframe/?redirect=true">install Google Chrome Frame</a> to better experience this site.</p>
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<section id="pageloader">
<div class="loader-item fa fa-spin colored-border"></div>
</section> <!-- /#pageloader -->
<header class="site-header container-fluid">
<div class="top-header">
<div class="logo col-md-6 col-sm-6">
<h1><a href="index.html"><em>Sasi </em>Bonu</a></h1>
<span> </span>
</div> <!-- /.logo -->
<div class="social-top col-md-6 col-sm-6">
<ul>
<li><a href="https://github.com/sasibonu" class="fa fa-github"></a></li>
<li><a href="https://www.linkedin.com/in/sasi-bonu-98878116a/" class="fa fa-linkedin"></a></li>
</ul>
</div> <!-- /.social-top -->
</div> <!-- /.top-header -->
<div class="main-header">
<div class="row">
<div class="main-header-left col-md-3 col-sm-6 col-xs-8">
<a id="search-icon" class="btn-left fa fa-search" href="#search-overlay"></a>
<div id="search-overlay">
<a href="#search-overlay" class="close-search"><i class="fa fa-times-circle"></i></a>
<div class="search-form-holder">
<h2>Type keywords and hit enter</h2>
<form id="search-form" action="#">
<input type="search" name="s" placeholder="" autocomplete="off" />
</form>
</div>
</div><!-- #search-overlay -->
<a href="#" class="btn-left arrow-left fa fa-angle-left"></a>
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<div class="menu-wrapper col-md-9 col-sm-6 col-xs-4">
<a href="#" class="toggle-menu visible-sm visible-xs"><i class="fa fa-bars"></i></a>
<ul class="sf-menu hidden-xs hidden-sm">
<li class="active"><a href="index.html">Introduction</a></li>
<li><a href="eda.html">EDA</a></li>
<li><a href="#">Projects</a>
<ul>
<li><a href="projects-2.html">Two Columns</a></li>
<li><a href="projects-3.html">Three Columns</a></li>
</ul>
</li>
<li><a href="conclusion.html">Conclusion</a></li>
<li><a href="contact.html">Contact</a></li>
</ul>
</div> <!-- /.menu-wrapper -->
</div> <!-- /.row -->
</div> <!-- /.main-header -->
<div id="responsive-menu">
<ul>
<li><a href="index.html">Introduction</a></li>
<li><a href="eda.html">EDA</a></li>
<li><a href="#">Projects</a>
<ul>
<li><a href="projects-2.html">Two Columns</a></li>
<li><a href="projects-3.html">Three Columns</a></li>
</ul>
</li>
<li><a href="conclusion.html">Conclusion</a></li>
<li><a href="contact.html">Contact</a></li>
</ul>
</div>
</header> <!-- /.site-header -->
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<div class="slider-caption">
<div class="inner-content">
<h2> People talk, let them!</h2>
<p> "Once you overcome the one-inch tall barrier of subtitles, you will be introduced to so many more amazing films." </p>
<p>- Bong Joon-Ho</p>
</div> <!-- /.inner-content -->
</div> <!-- /.slider-caption -->
</div> <!-- /.swier-slide -->
<div class="swiper-slide" style="background-image: url(images/slide2.jpeg);">
<div class="overlay-s"></div>
<div class="slider-caption">
<div class="inner-content">
<h2> </h2>
<p> Linguistic limitations make it difficult to communicate effectively in the connected world of today, both in person and online. Thankfully, machine learning advances provide hopeful answers. This machine learning model for speech translation serves as a remedy in this field. It will make use of natural language processing to enable smooth communication between different cultural groups. It will accurately translate spoken language in real-time via algorithms. Both precision and flow will be considered in its architecture. This transforming quality of the model enables people to interact and cooperate across language barriers. The era of global communication is about to begin. It aims to strengthen links and promote mutual understanding between various groups and cultural traditions. People from different cultures can listen to artists and films because of this.</p>
</div> <!-- /.inner-content -->
</div> <!-- /.slider-caption -->
</div> <!-- /.swier-slide -->
<div class="swiper-slide" style="background-image: url(images/slide3.jpeg);">
<div class="overlay-s"></div>
<div class="slider-caption">
<div class="inner-content">
<h2></h2>
<p>Even though it’s mainly designed for films, the speech translating ML model holds immense potential across various sectors and scenarios. Its applications range from global business meetings to everyday conversations. Educational institutions can leverage it for language learning, enhancing access and comprehension. In emergencies like natural disasters or medical crises, it facilitates vital communication between responders and affected individuals. It enriches entertainment and media by offering multilingual subtitles and dubbing. Its versatility and accuracy make it indispensable in fostering global connectivity and understanding. Text can be translated using technology, but I frequently find the results to be wrong, especially when it comes to languages that aren't widely spoken. A few machine translations that are accessible also have very identical problems.</p>
</div> <!-- /.inner-content -->
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</div> <!-- /.swier-slide -->
<div class="swiper-slide" style="background-image: url(images/slide3.jpeg);">
<div class="overlay-s"></div>
<div class="slider-caption">
<div class="inner-content">
<h2></h2>
<p>Multiple algorithms are being used to test and train the data. To carry out the entire function or just a portion of it. It begins with unsupervised techniques like PCA, Hierarchical Clustering, and K-Means Clustering. Subsequently, supervised learning techniques such as Decision Trees, Neural Networks, Naive Bayes, and SVM are employed. Each page has a unique conclusion section that determines whether or not the plan of action was successful. This is based on predefined criteria and metrics. To train the final data, whichever method or combination of methods works best will be adopted. The decision on the final model is driven by performance metrics, such as accuracy, precision, etc. It will be tweaked further to improve accuracy, underfitting, or overfitting.</p>
</div> <!-- /.inner-content -->
</div> <!-- /.slider-caption -->
</div> <!-- /.swier-slide -->
<div class="swiper-slide" style="background-image: url(images/slide4.jpeg);">
<div class="overlay-s"></div>
<div class="slider-caption">
<div class="inner-content">
<h2>Questions to ask</h2>
<ul>
<li> Who is the target audience for the translation?</li>
<li> How can one review the accuracy for languages they can't speak? </li>
<li> What are the linguistic and cultural nuances of the source and target languages?</li>
<li> Is the speech formal?</li>
<li> Are there any domain-specific terms in the speech?</li>
<li> Does the speech contain any ambiguity?</li>
<li> Are there any constraints or limitations (e.g. time) for the translation process?</li>
<li> How will the translation be delivered or presented?</li>
<li> Has the translation been reviewed and revised for accuracy and fluency?</li>
<li> What feedback or input can be gathered from native speakers or language experts?</li>
</ul>
</div> <!-- /.inner-content -->
</div> <!-- /.slider-caption -->
</div> <!-- /.swier-slide -->
<div class="swiper-slide" style="background-image: url(images/slide3.jpeg);">
<div class="overlay-s"></div>
<div class="slider-caption">
<div class="inner-content">
<h2></h2>
<p>
Data has generally been split into training and testing categories. The training class has the bulk of the data.
This data is being used to train the model, in other words, to teach the model. The testing set is being used to test the performance.
The main criterion to judge the model is the accuracy. The data is mainly textual, hence it is sure the ML algorithms won't do well.
This is to be said as ML algorithms work best on numerical data. The best to use would most probably be Neural Networks.
There will be analysis, nonetheless, of all the ML algorithms in doing the impossible. To maybe look for answers where no one has looked for.
</p>
</div> <!-- /.inner-content -->
</div> <!-- /.slider-caption -->
</div> <!-- /.swier-slide -->
</div> <!-- /.swiper-wrapper -->
</div> <!-- /.swiper-container -->
<script src="js/vendor/jquery-1.11.0.min.js"></script>
<script>window.jQuery || document.write('<script src="js/vendor/jquery-1.11.0.min.js"><\/script>')</script>
<script src="js/plugins.js"></script>
<script src="js/main.js"></script>
<!-- Preloader -->
<script type="text/javascript">
//<![CDATA[
$(window).load(function() { // makes sure the whole site is loaded
$('.loader-item').fadeOut(); // will first fade out the loading animation
$('#pageloader').delay(350).fadeOut('slow'); // will fade out the white DIV that covers the website.
$('body').delay(350).css({'overflow-y':'visible'});
})
//]]>
</script>
</body>
</html>