Lecture 5.5 ELMo, Word2Vec

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ADL Lecture 5: 1:31:30

ADL Lecture 5: Word Embeddings (19/03/26)

ELMo vs BERT 0:00:45

ELMo vs BERT

Word2Vec, GloVe, FastText- 0:13:20

Word2Vec, GloVe, FastText- EXPLAINED!

Word Embeddings - 0:10:06

Word Embeddings - EXPLAINED!

Sentiment Classification with 0:05:17

Sentiment Classification with ELMO Deep Contextualized Word Vectors

Word Embedding and 0:16:12

Word Embedding and Word2Vec, Clearly Explained!!!

SMLTAR: Word Embeddings 0:33:28

SMLTAR: Word Embeddings Part 1 (smltar01 5)

Word Representation Learning 0:42:03

Word Representation Learning

Programming for AI 1:46:52

Programming for AI (AI504, Fall 2020), Class 8: Word Embedding

What are word 0:01:00

What are word embeddings?

Language Model Overview: 1:10:26

Language Model Overview: From word2vec to BERT

NLPE3: Word Embeddings 0:21:42

NLPE3: Word Embeddings Beyond Word2Vec

Accelerated Natural Language 0:07:02

Accelerated Natural Language Processing 3.2 - Word Vectors

ELMO Embeddings - 1:35:55

ELMO Embeddings - Team unfound.news

Semantic Search using 0:22:59

Semantic Search using Elmo Embeddings

The Biggest Misconception 0:04:43

The Biggest Misconception about Embeddings

L12: Advanced NLP 2:16:30

L12: Advanced NLP (continued): BERT, ELMo et al and revisiting GANs

Vectoring Words (Word 0:16:56

Vectoring Words (Word Embeddings) - Computerphile

What is BERT 0:00:50

What is BERT

Text Extraction using 0:04:03

Text Extraction using Elmo

NLP Tutorial 18 0:36:06

NLP Tutorial 18 | word2vec Word Embedding with SpaCy

Embeddings Debate 2020 0:05:58

Embeddings Debate 2020 | word2vec vs GloVe

What is ELMo? 0:00:41

What is ELMo? #bert #chatgpt #deeplearning

Word2Vec and Natural 0:03:06

Word2Vec and Natural Language Processing NLP - Overview

Комментарии
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Great video, this explains it perfectly

itznukeey
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Thank you. Very good explanation of ELMo

abhishektyagi
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I have question: I want to do something like this:

"Fire" + "Mountain" --> "Volcano"
"Volcano" --> "Fire", "Mountain", "Lava"
"Fire" + "Steel" + "Building" --> "Forge"

Is this possible with technology discussed in your video? Does it come down to the corpus I train with? Can I extend those models with semantic lexica like WordNet to get other relations of words then similarities? <3

seventfour
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word "compare" is one-hot (shape 1000*1), suppose [0, 0, 1, ...0, 0] (the third element is 1), so, the embedding matrix is 300*1000 to do matrix dot product (300*1000 * 1000*1 = 300*1). Since most of the elements are 0, how to get a meaningful embedding matrix? (0 product everything is 0)

junderfitting
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Models are based on historic information. There’s no bias.

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