With the advent of machine learning frameworks like Tensorflow and PyTorch, and the rise of the "Big Data" trend in the past decade, Python has quickly become the #1 choice of language for a lot of scientists and ML practitioners, who are continuously working with different prototypes of data analysis and model training pipelines. This is mainly due to its simple syntax and interpreted nature, that allows for shorter prototyping and experimentation cycles. However, this massive influx into Python usually resulted in a biased paradigm of programming, where the developer would attempt to write Python the way they would another language they are more used to. Albeit harmless most of the time, it can be very inefficient, and could be a waste of potential of what can be achieved by Python's largely unknown native modules. Therefore, I hope to present some of the more "Pythonic" programming techniques that I hope you find useful and inspiring next time you plan to create some binary magic.
CERN openlab Summer Student Programme 2022
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