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    <title>Data science on Benoit Pasquier</title>
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    <description>Recent content in Data science on Benoit Pasquier</description>
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      <title>How to make your iOS app smarter with sentiment analysis</title>
      <link>https://benoitpasquier.com/2020/01/how-to-make-your-ios-app-smarter-with-sentiment-analysis/</link>
      <pubDate>Tue, 28 Jan 2020 08:00:00 +0000</pubDate>
      
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      <description>&lt;p&gt;For quite some time now, I&amp;rsquo;ve been developing an interest to data analysis to find new ways to improve mobile app. I&amp;rsquo;ve recently found some time to experiment neural language processing for a very specific usecase related to my daily work, sentiment analysis of customer reviews on fashion items.&lt;/p&gt;</description>
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      <title>Create a machine learning model to classify Fashion images in Swift</title>
      <link>https://benoitpasquier.com/machine-learning-fashion-swift/</link>
      <pubDate>Sun, 10 Feb 2019 08:00:00 +0000</pubDate>
      
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      <description>&lt;p&gt;Since WWDC18, Apple made it way easier to developers to create model for machine learning to integrate iOS apps. I have tried myself in the past different models, one for face detection and create another with Tensorflow to fashion classification during a &lt;a href=&#34;https://benoitpasquier.com/image-classification-tensorflow-hackathon&#34;&gt;hackathon&lt;/a&gt;. Today I’ll share with you how I create a model dedicated to fashion brands.&lt;/p&gt;</description>
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      <title>Build a visual search app with TensorFlow in less than 24 hours</title>
      <link>https://benoitpasquier.com/image-classification-tensorflow-hackathon/</link>
      <pubDate>Thu, 08 Nov 2018 08:00:00 +0000</pubDate>
      
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      <description>&lt;p&gt;For a while now, I really wanted to work on a machine learning project, especially since Apple let you import trained model in your iOS app now. Last September, I took part of a 24h hackathon for an e-commerce business, that was my chance to test it. The idea was simple: a visual search app, listing similar products based on a picture.&lt;/p&gt;</description>
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