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Natural Language Processing (NLP) with PyTorch

.. toctree::
   :maxdepth: 2
   :hidden:
   :caption: Extra Resources

   download_data
   environment_setup
   faq
   migration

.. toctree::
   :hidden:
   :caption: Day 1 Materials

   day1/takehome
   day1/solutions

.. toctree::
   :hidden:
   :maxdepth: 3
   :caption: Day 2 Materials

   day2/warmup
   day2/failfastprototypemode
   day2/tensorfu1
   day2/tensorfu2
   day2/patterns/tidbits
   day2/adventures/lookups
   day2/adventures/interpolation
   day2/sampling


Hello! This is a directory of resources for a training tutorial to be given at the O'Reilly AI Conference in London on Monday, October 8th, and Tuesday, October 9th.

Please read below for general information. You can find the github repository at this link. Please note that there are two ways to engage in this training (described below).

More information will be added to this site as the training progresses. Specifically, we will be adding a 'recipes' section, 'errata' section, and a 'bonus exercise' section as the training progresses!

General Information

Prerequisites:

  • A working knowledge of Python and the command line
  • Familiarity with precalc math (multiply matrices, dot products of vectors, etc.) and derivatives of simple functions (If you are new to linear algebra, this video course is handy.)
  • A general understanding of machine learning (setting up experiments, evaluation, etc.) (useful but not required)

Hardware and/or installation requirements:

  • There are two options:
    1. Using O'Reilly's online resources. For this, you only needs a laptop; on the first day, we will provide you with credentials and a URL to use an online computing resource (a JupyterHub instance) provided by O'Reilly. You will be able to access Jupyter notebooks through this and they will persist until the end of the second day of training. This option is not limited by what operating system you have. You will need to have a browser installed.
    2. Setting everything up locally. For this, you need a laptop with the PyTorch environment set up. This is only recommended if you want to have the environment locally or have a laptop with a GPU. (If you have trouble following the provided instructions or if you find any mistakes, please file an issue here.)