Tag: data-science

The more you use it, the more you love it

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A quick guide on how to do topic modeling with Python

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According to John Chambers, everything that exists is an object in R and everything that happens is a function call. This summarizes the philosophy of R. Knowing this, we can understand R better.

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There are many packages in R or Python to deal with table like data (or so called data frame), but data.table is probably the most efficient one. Here are some best practices to use data.table.

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There are many projects in the European Union that are funded by the European Research and Innovation Programme. To make sense of those projects and their results, we have to do lots of data processing and analysis.

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Training and tuning a deep learning model is a complex process. This post will cover the basics of how to initialize the parameters of a deep learning model.

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As key components of neural networks, activation functions are responsible for transforming the input data into the desired output. In this article, we will discuss the most common activation functions and their applications.

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A guide to building a full stack deep learning application with PyTorch in a small scale, from data collection to model saving without deploying to production.

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Embracing the python and R community together needs smooth transference of our mentalities and skills from both community; I believe one could get best of both worlds in VS Code.

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It took me a while to get into patent analytics, which is a 'wild' world. I hope my guide for patent analytics could help you to navigate through.

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What should we do when we realize some packages from Python or R could not be installed in our local machine?

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