Spring 2017 CS292F Syllabus
From courses
Revision as of 21:08, 25 March 2017 by Wangwilliamyang (talk | contribs) (Created page with "*04/04 Introduction, logistics, NLP, and deep learning. *04/06 Tips for a successful class project *04/11 Word embeddings (HW1 out) ** [https://ronan.collobert.com/pub/matos/...")
- 04/04 Introduction, logistics, NLP, and deep learning.
- 04/06 Tips for a successful class project
- 04/11 Word embeddings (HW1 out)
- A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning, Collobert and Weston, ICML 2008
- Distributed Representations of Words and Phrases and their Compositionality, T Mikolov, I Sutskever, K Chen, GS Corrado, J Dean, NIPS 2013
- Glove: Global Vectors for Word Representation, J Pennington, R Socher, CD Manning - EMNLP, 2014
- 04/13 Knowledge base embeddings
- 04/18 Neural network basics (Project proposal due)
- 04/20 Neural networks language models
- 04/25 RNNs (HW1 due and HW2 out)
- 04/27 LSTMs/GRUs
- 05/02 Convolutional Neural Networks (1)
- 05/04 Convolutional Neural Networks (2)
- 05/09 Project: mid-term presentation (1)
- 05/11 Project: mid-term presentation (2) (HW2 due)
- 05/16 Language and vision
- 05/18 Information extraction
- 05/23 Speech recognition and understanding
- 05/25 Sequence-to-sequence models and neural machine translation
- 05/30 Attention mechanisms in NLP
- 06/01 Question answering
- 06/06 Project: final presentation (1)
- 06/08 Project: final presentation (2)
