Deep learning has gotten a lot easier ever since Keras came on the scene a few years ago (you should be using
tf.keras). Spacy has made NLP a breeze, and the similar-sounding scrapy even lets you assemble your own datasets. Then there are the corporate players like Apple’s Turi create and Google’s Tensorflow 2. You also can’t forget scikit-learn, which everyone uses. Then there’s good old NLTK and OpenCV’s Python API. Resting beneath all of these tools are numpy and scipy. And who could live without pandas, or maybe pyspark if you’re working with huge datasets.
But there’s one library that’s more useful than any of these. It gets used when we’re collecting data sets. It’s everywhere when we’re cleaning and munging data. It rears its head during exploratory analysis and modeling. It even comes into play when we create visualizations. This is Python’s regular expression library,
re, and it’s fundamental to every AI system and process.