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	<title>CBOW Archives - Petamind</title>
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		<title>Word2vec with TensorFlow 2.0 &#8211; a simple CBOW implementation</title>
		<link>https://petaminds.com/word2vec-with-tensorflow-2-0-a-simple-cbow-implementation/</link>
					<comments>https://petaminds.com/word2vec-with-tensorflow-2-0-a-simple-cbow-implementation/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sat, 19 Oct 2019 23:30:20 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[CBOW]]></category>
		<category><![CDATA[keras]]></category>
		<category><![CDATA[natural language processing]]></category>
		<category><![CDATA[neural network]]></category>
		<category><![CDATA[tenso]]></category>
		<category><![CDATA[word2vec]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1144</guid>

					<description><![CDATA[<p>In TensorFlow website, there is a good example of word embedding implementation with Keras. Nevertheless, we are curious to see how it looks like when implementing word2vec with PURE TensorFlow 2.0. What is CBOW In the previous article, we introduced Word2vec (w2v) with Gensim library. Word2vec consists of two-layer neural networks that are trained to reconstruct linguistic [&#8230;]</p>
<p>The post <a href="https://petaminds.com/word2vec-with-tensorflow-2-0-a-simple-cbow-implementation/">Word2vec with TensorFlow 2.0 &#8211; a simple CBOW implementation</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<title>Word2vec with gensim &#8211; a simple word embedding example</title>
		<link>https://petaminds.com/word2vec-with-gensim-a-simple-word-embedding-example/</link>
					<comments>https://petaminds.com/word2vec-with-gensim-a-simple-word-embedding-example/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Wed, 11 Apr 2018 05:58:27 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[CBOW]]></category>
		<category><![CDATA[GENSIM]]></category>
		<category><![CDATA[neural network]]></category>
		<category><![CDATA[NLP]]></category>
		<category><![CDATA[skip-grams]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1127</guid>

					<description><![CDATA[<p>In this short article, we show a simple example of how to use GenSim and word2vec for word embedding. Word2vec Word2vec is a famous algorithm for natural language processing (NLP) created by Tomas Mikolov teams. It is a group of related models that are used to produce&#160;word embeddings, i.e. CBOW and skip-grams. The models are [&#8230;]</p>
<p>The post <a href="https://petaminds.com/word2vec-with-gensim-a-simple-word-embedding-example/">Word2vec with gensim &#8211; a simple word embedding example</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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