Difference between revisions of "User:Cnapun"

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(Week 2 (12 Jun - 16 Jun))
(Weekly Log)
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* Things I got working in Tensorflow:
 
* Things I got working in Tensorflow:
 
** n-layer Sequence to sequence (seq2seq) model (encoder-deocder architecture)
 
** n-layer Sequence to sequence (seq2seq) model (encoder-deocder architecture)
 +
** Autoregressive seq2seq model (using slower ops)
 +
** Multilayer Perceptron
 +
** 1D ConvNet on weather inputs, in parallel with MLP to process load and time inputs
 +
* Will try ConvNet on all 3 inputs, feeding into RNN, with the seed state being the month, day, and day of week at the initial timestamp
 +
* Will try Clockwork RNN
  
 
== Reading ==
 
== Reading ==

Revision as of 19:38, 16 June 2017

Personal Information

  • Case Western Reserve University Class of 2019
  • Applied Mathematics and Computer Science Major

Weekly Log

Week 0 (30 May - 2 Jun)

  • Met Dr. Povinelli 4 times: to discuss possible project topics, to get oriented with the lab, to decide on a project topic, and to establish weekly milestones
  • Obtained MU and MSCS account logins and ID card
  • Read most of "Learning Deep Architectures for AI"
  • Read section on Sequence Modeling (Ch 10) of The Deep Learning book
  • Read various other papers

Week 1 (5 Jun - 9 Jun)

  • Attended GasDay camp and learned about what GasDay does
  • Attended responsible conduct of research training
  • Continued to read papers
  • Decided to use TensorFlow and Keras for now
  • Got Anaconda, TensorFlow, and Keras installed on a couple lab computers
  • Learned how to access customer data

Week 2 (12 Jun - 16 Jun)

  • Started playing with TensorFlow, mostly using the GEFCom2014-E dataset to allow me to continue working when not at the lab
  • Things I got working in Tensorflow:
    • n-layer Sequence to sequence (seq2seq) model (encoder-deocder architecture)
    • Autoregressive seq2seq model (using slower ops)
    • Multilayer Perceptron
    • 1D ConvNet on weather inputs, in parallel with MLP to process load and time inputs
  • Will try ConvNet on all 3 inputs, feeding into RNN, with the seed state being the month, day, and day of week at the initial timestamp
  • Will try Clockwork RNN

Reading

Here are some of the papers I have read, skimmed, and partially read:

Forecasting with Deep Learning

Hybrid Methods

Review Papers

LSTMs, Training, and Possible Improvements