NLP and Deep Learning 1: Human Language & Word Vectors	 thumbnail
slide-image
Pause
Mute
Subtitles not available
Playback speed
0.25
0.5
0.75
1
1.25
1.5
1.75
2
Full screen

NLP and Deep Learning 1: Human Language & Word Vectors

Published on Sep 13, 201519338 Views

Related categories

Chapter list

NLP and Deep Learning 1: Human Language & Word Vectors00:00
My plan00:45
The nature of human language02:38
What’s special about human language? - 102:50
What’s special about human language? - 205:08
What’s special about human language? - 308:03
What’s special about human language? - 408:49
From symbolic to distributed and back 09:50
Distributional word representations: Introduction10:56
From symbolic to distributed representations - 111:18
From symbolic to distributed representations - 212:05
From symbolic to distributed representations - 313:27
Basic idea of learning neural word embeddings14:46
With distributed, distributional representations, syntactic and semantic similarity is captured - 115:47
With distributed, distributional representations, syntactic and semantic similarity is captured - 216:56
Word Analogies - 119:12
Word Analogies - 220:52
Distributional representations can solve the fragility of NLP tools - 122:28
Distributional representations can solve the fragility of NLP tools - 224:09
Neural embeddings 24:33
word2vec - 128:37
Word2vec recipe28:51
word2vec - 229:21
Word2vec (SkipGram) 40:51
Naïve Algorithm 41:06
Skip Gram Negative Sampling - 144:36
Skip Gram Negative Sampling - 244:57
word2vec is a matrix factorization 44:58
Other neural embeddings work - 146:21
Other neural embeddings work - 248:57
Comparison with LSA & GloVe49:06
LSA vs. word2vec - 149:54
LSA vs. word2vec - 249:59
COALS model (count-modified LSA)50:59
Count based vs. direct prediction52:31
Encoding meaning in vector differences - 152:39
Encoding meaning in vector differences - 253:55
Encoding meaning in vector differences - 354:08
GloVe: A new model for learning word representations54:44
Word Embeddings: Summary59:52
Analogy evaluation and hyperparameters01:00:35
Word Embeddings: Word senses01:01:25
Word Embeddings: Multi-words01:08:17
Representations and levels in linguistics01:13:45
Phonetics and phonology - 101:14:06
Phonetics and phonology - 201:16:22
Writing systems - 101:18:19
Writing systems - 201:20:47
Morphology - 101:22:37
Morphology - 201:23:23
Syntax: Sentence structure 01:24:04
Semantics01:25:53
Semantics and Pragmatics 01:26:35