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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
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
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
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
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
CS224d: Deep Learning for Natural Language Processing Reports for 2015 Project NameAuthors Classifying responses on online discussion forums Aaron Abajian Opinion Tagging Using Deep Recurrent Nets with GRUs Alex Adamson / Vehbi Deger Turan Entity Level Sentiment Analysis for Amazon Web Reviews Y. Ahres / N. Volk MT using RNNs enriched with Universal Anonymous Selecting Best Answers from Question-A
Schedule and Syllabus Unless otherwise specified the course lectures and meeting times are: Tuesday, Thursday 3:00-4:20 Location: Gates B1
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