5 Amazing NLP Practical Resources – Part 1

Are you interested in some practical resources for NLP (natural language Processing)? There are so many online NLP resources, especially those that rely on deep learning approaches, that it can be quite a task to sift through to find the quality. There are some well-known, top-notch, mainly theoretical resources with deep learning courses, especially the Stanford and Oxford NLP: 

  • Natural Language Processing with Deep Learning (Stanford)
  • Deep Learning for Natural Language Processing (Oxford)

But what if you have completed these, have already gained a foundation in NLP and want to move to some practical resources, or are you simply interested in other approaches that may not necessarily depend on neural networks? This post is going to be helpful (hopefully).

  1. Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit

This is the introductory natural language processing book, at least from the dual practicality perspective and the ecosystem of Python. The book approaches NLP, as the title suggests, using the Natural Language Toolkit (NLTK), which you either already heard about or need to learn about immediately. The best part of the book is it gets right; no mess around, just lots of code and concepts.

  1. Deep Learning for Natural Language Processing: Tutorials with Jupyter Notebooks

This is a repo of the Jupyter notebooks that accompany the fantastic set of deep learning videos for NLP by Jon Krohn. The notebooks are lifted directly from his video walkthroughs, so you don’t miss out on anything as far as content is concerned. Protip: if you are interested in watching his videos-offered via the Safari platform of O’Reilly-sign up for a free 10-day trial and rip through the few hours of video before it expires. Here is an overview of what Jon is covering in these notebooks and the accompanying videos if you want to learn how to:

  • Natural language data preprocessing for use in machine learning applications.
  • Transform natural language (with word2vec) into numerical representations.
  • Make predictions with natural language-trained deep learning models.
  • Use advanced NLP approaches with Keras, the TensorFlow API at the highest level.
  • Improve the performance of deep learning models by tuning hyperparameters.

It’s a great way to kill a long afternoon by combining notebooks, videos, and your own environment to follow in.

Keep watching this space for more.

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