Deep Learning for Natural Language Processing - 7th Meetup
Details
The 7th meeting of our CS224d: Deep Learning for Natural Language Processing (http://cs224d.stanford.edu/) series will be Thursday 12/1/2016 from 6-8 pm.
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We will meet again at Comcast, and we thank them again. Their building doors close at 6 pm, but there will be a Comcast employee at the entrance until 6:15 pm. Please bring your photo ID.
On Thursday we will send a phone number to attendees in case you need to arrive later - we'll then send someone out of the meeting to escort you up.
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About this meeting:
Our 6th meeting was a walk-through of the code for problems 3, word2vec, and 4, Sentiment Analysis. In this 7th meeting, we'll go over, Lecture 7, Introduction to TensorFlow (https://www.youtube.com/watch?v=L8Y2_Cq2X5s&index=7&list=PLmImxx8Char9Ig0ZHSyTqGsdhb9weEGam). So after getting through the basic technical background of deep neural nets in our previous meetings, we'll now start learning the exciting part - how to build these models.
Please view the YouTube video (https://www.youtube.com/watch?v=L8Y2_Cq2X5s&list=PLmImxx8Char9Ig0ZHSyTqGsdhb9weEGam&index=7) for this lecture before the meeting; companion slides are here (http://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf). It might be interesting for us to read the Google Research Blog, Celebrating TensorFlow's First Year (https://research.googleblog.com/2016/11/celebrating-tensorflows-first-year.html).
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We are continuing to add content to our shared GitHub repository (https://github.com/dcdlwg/stanford_cs224d) at DC DLWG (https://github.com/dcdlwg). The solutions to problems we've gone over in our meetings have been uploaded to the repo, and we will continue to share resources and code there as we progress through the course. You can share your own work by creating your own repo at DC DLWG (https://github.com/dcdlwg). So please create a GitHub account when you get a chance, if you don’t have one already.
We try to accommodate all levels of experience but the course we are following assumes familiarity with the following: Proficiency in Python, calculus, linear algebra, basic probability and statistics and some concepts of machine learning. Please look through the course website (http://cs224d.stanford.edu/) to see if this is the right level for you.