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Wrangle your data with Python

#artificialintelligence

Learn techniques for programmatically acquiring data and how to extract that data. Finding your first dataset(s) to investigate might be the most important step toward acheiving your goal of answering your questions. As we mentioned in not available, you should first spend some time refining your question until you have one specific enough to identify good data about but broad enough to be interesting to you and others. Alternatively, you might have a dataset you already find interesting, but no compelling question. If you don't already know and trust the data source, you should spend some time investigating.


mahmoudnafifi/WB_sRGB

#artificialintelligence

Reference code for the paper When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images. The original source code of our paper was written in Matlab. We provide a Python version of our code. We tried to make both versions identical. However, there is no guarantee that the Python version will give exactly the same results.


t-SNE algo in R and Python, made with same dataset

@machinelearnbot

The technique can be implemented via Barnes-Hut approximations, allowing it to be applied on large real-world datasets.


t-SNE algo in R and Python, made with same dataset

#artificialintelligence

The technique can be implemented via Barnes-Hut approximations, allowing it to be applied on large real-world datasets.