Lecture10-RvNNs
CS224d: Deep NLP Lecture 9: Wrap up: LSTMs and Recursive Neural Networks Richard Socher richard@metamind.io Overview 4/29/16 Richard Socher 2 Video issues and fire alarm Finish LSTMs Recursive Neural Networks • Motivation:Compositionality • Structure prediction:Parsing • Backpropagation through Structure • Vision Example Next Lecture: • RvNN improvements Long-short-term-memories (LSTMs) 4/29/16 Ri
Lecture 13-CNNs
CS224d: Deep NLP Lecture 13: Convolutional Neural Networks (for NLP) Richard Socher richard@metamind.io Overview of today • From RNNs to CNNs • CNN Variant 1: Simple single layer • Application:Sentence classification • More details and tricks • Evaluation • Comparison between sentence models: BoV, RNNs2, CNNs • CNN Variant 2: Complex multi layer 5/12/16 Richard Socher From RNNs to CNNs 5/12/16 Ric
vanishing_grad_example
In this notebook, we will demonstrate the difference between using sigmoid and ReLU nonlinearities in a simple neural network with two hidden layers. This notebook is built off of a minimal net demo done by Andrej Karpathy for CS 231n, which you can check out here: http://cs231n.github.io/neural-networks-case-study/¶ # Setup import numpy as np import matplotlib.pyplot as plt %matplotlib inline plt
Lecture6
CS224d Deep NLP Lecture 6: Neural Tips and Tricks + Recurrent Neural Networks Richard Socher richard@metamind.io Overview Today: • Another explanation of backprop (from a tutorial Yoshua, Chris and I did) • Practical tips and tricks: • Multi-task learning • Nonlinearities • Finite difference gradient check • Momentum, AdaGrad • Language Models • First intro to Recurrent Neural Networks 4/17/16 Ric
A Neural Conversational Model
arXiv:1506.05869v3 [cs.CL] 22 Jul 2015 A Neural Conversational Model Oriol Vinyals VINYALS@GOOGLE.COM Google Quoc V. Le QVL@GOOGLE.COM Google Abstract Conversational modeling is an important task in natural language understanding and machine in- telligence. Although previous approaches ex- ist, they are often restricted to specific domains (e.g., booking an airline ticket) and require hand- crafte
Lecture9-LSTMs
CS224d Deep NLP Lecture 8: Recap, Projects and Fancy Recurrent Neural Networks for Machine Translation Richard Socher richard@metamind.io Overview 4/26/16 Richard Socher 2 • Projects • Recap of most important concepts & equations • Wrap up: Deep RNNs and F1 evaluation • Machine translation • Fancy RNN Models tackling MT: • Gated Recurrent Units by Cho et al. (2014) • Long-Short-Term-Memories by Ho
Lecture8-RNNs
CS224d Deep NLP Lecture 8: Recurrent Neural Networks Richard Socher richard@metamind.io Overview 4/21/16 Richard Socher 2 • Feedback • Traditional language models • RNNs • RNN language models • Important training problems and tricks • Vanishingand explodinggradient problems • RNNs for other sequence tasks • Bidirectionaland deep RNNs Feedback 4/21/16 Richard Socher 3 Feedback à Super useful à Than
Stanford University CS224d: Deep Learning for Natural Language Processing
Please see cs224n.stanford.edu for the current (Winter 2017) version of this class. Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, email
Index of /lectures
Index of /lectures NameLast modifiedSizeDescription Parent Directory - CS224D-Lecture7-2.pdf2016-04-19 16:06 77K CS224d-Lecture1.pdf2016-03-31 12:03 11M CS224d-Lecture2.pdf2016-03-31 12:03 5.0M CS224d-Lecture3.pdf2016-04-05 15:18 6.2M CS224d-Lecture 4.pdf2016-04-05 15:21 2.7M CS224d-Lecture4.pdf2016-04-07 13:16 2.6M CS224d-Lecture5.pdf2016-04-12 14:46 2.9M CS224d-Lecture6.pdf2016-04-17 18:52 4.2M
Stanford University CS224d: Deep Learning for Natural Language Processing
Please see cs224n.stanford.edu for the current (Winter 2017) version of this class. Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, email
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https://cs224d.stanford.edu/reports/GreensteinEric.pdf
https://cs224d.stanford.edu/reports/Lewis.pdf
http://cs224d.stanford.edu/lectures/CS224d-Lecture2.pdf
Stanford University CS224d: Deep Learning for Natural Language Processing
http://cs224d.stanford.edu/reports/SkiZhengshan.pdf
https://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf
http://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf
https://cs224d.stanford.edu/reports/LiuShenxiu.pdf
https://cs224d.stanford.edu/reports/HongJames.pdf
https://cs224d.stanford.edu/reports/SongWilliam.pdf