Modern natural language processing (NLP) workflows often require interoperability between multiple tools, with components like SpaCy for stochastic parsing, TensorFlow for deep learning, and JavaScript or D3 for visualization. Aaron Kramer offers an introduction to interactive NLP with SpaCy within the Jupyter Notebook, covering core NLP concepts, core workflows in SpaCy, and examples of interacting with other tools like TensorFlow, NetworkX, LIME, and others as part of interactive NLP projects. Along the way, Aaron walks you through training a deep learning mode, loading it into SpaCy, and explaining your NLP models with LIME.
Aaron Kramer is a data scientist and engineer at DataScience.com, where he builds powerful language and engagement models using natural language processing, deep learning, Bayesian inference, and machine learning.
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Hi everyone! Looking forward to our session tomorrow.
Please remember to have git and docker set up!
Setup instructions are here: https://github.com/datascienceinc/jupytercon-2017#jupytercon-2017