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(This article was first published on <strong><a href="http://jasdumas.github.io/2018-02-20-deep-learning-img-classifier/"> Jasmine Dumas' R Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>I have to admit my initial thoughts of deep learning were pessimistic and in order to not succumb to impostor syndrome, I put off learning any new techniques in the growing sub field of machine learning, until recently. After attending &amp; speaking at <a rel="nofollow" href="http://datadaytexas.com/" target="_blank">Data Day Texas</a> and listening to Lukas Biewald’s Keynote titled: <a rel="nofollow" href="https://www.slideshare.net/lbiewald/deep-learning-in-the-real-world" target="_blank">Deep Learning in the Real World</a>, I began to see through the complexities of Deep Learning and understand the real world applications. My favorite example from the keynote was <a rel="nofollow" href="https://developers.googleblog.com/2017/09/how-machine-learning-with-tensorflow.html" target="_blank">Coca Cola deploying a deep learning model</a> to easily capture under the cap promotional codes. I left the conference with some initial ideas about detecting deer in my backyard using a web cam and running a image classification algorithm as my first step into <em>learning by doing</em>.</p>
<p>For this image classification project I leveraged a pre-trained model from the <code>R</code> interface to <a rel="nofollow" href="https://keras.rstudio.com/index.html" target="_blank">Keras</a>, that had been previously trained on a similar task. This enabled me to prototype something quickly and cheaply in a weekend and wrap the code as an interactive web app with a <code>shiny</code> flexdashboard. Here is the link to the <code>shiny</code> app which enables you to upload a image and return the top 3 predicted classes for that image: <a rel="nofollow" href="https://jasminedumas.shinyapps.io/image_clf/" target="_blank">https://jasminedumas.shinyapps.io/image_clf/</a> and a preview of the app in action below.</p>
<p><img alt="alt text" width="456 px" src="https://i2.wp.com/jasdumas.github.io/post_data/img_clf_app.jpeg?resize=456%2C304" data-recalc-dims="1" height="304 px"></p>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://jasdumas.github.io/2018-02-20-deep-learning-img-classifier/"> Jasmine Dumas' R Blog</a></strong>.</div>
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