In a previous blog post
I demonstrated how the vector datatype in Elasticsearch can be used to search words by their semantic meaning.
In this post I will show how a reverse image search for paintings can be implemented using the same methods.
Given a photo of a painting, we will use Elasticsearch to find other paintings which look similar.
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You heard all the potentials that Machine Learning (ML) is capable.
Detecting fraud, predicting machine failure or understanding customer behavior.
ML delivers tremendous impact to a variety of businesses.
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Data scientists often find themselves spending a lot of time with data acquisition and preparation,
yet most tutorials start with ready to use datasets.
This time we will start with nothing but a simple problem and gather the data with scrapy to provide insight into the
process from data gathering to model creation.
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Text classification is a common task where machine learning is applied.
Be it questions on a Q&A platform, a support request, an insurance claim or a business inquiry - all of these are usually written in free
form text and use vocabulary which might be specific to a certain field.
This article demonstrates how such classification problems can be tackled with the open source neural network library Keras.
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One of the goals I set for this year is to explore Machine Learning (ML), so after having done a couple of courses here and there, I decided to do a -rather simple- starting project, where I could deal with some of the basic stages of the ML: Get the data, prepare it, choose a model, train it, evaluate it, export it, and make the predictions available for use.
For this first project, I chose:
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