Mastering Self-Attention in Machine Learning 2021: Exploring Transformer Networks (Part 1)

Name: Mastering Self-Attention in Machine Learning 2021: Exploring Transformer Networks (Part 1)
Uploaded: 2023-04-17 14:03:33
Description: Self-Attention technology and the Transformer network architecture can efficiently process varying input sizes and lengths in complex problems such as word processing by calculating correlations between vectors in a sequence.

This article is a summary of a YouTube video "【機器學習2021】自注意力機制 (Self-attention) (上)" by Hung-yi Lee

TLDR Self-Attention technology and the Transformer network architecture can efficiently process varying input sizes and lengths in complex problems such as word processing by calculating correlations between vectors in a sequence.

Timestamped Summary

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00:00
Self-Attention is a network architecture that solves the problem of varying input sizes and lengths in complex problems such as word processing.
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03:06
Word Embedding can simplify the complexity of sound signals and graphs by representing them as vectors, which can be used for regression or classification problems.
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07:56
POS Tagging is the challenge of identifying the part-of-speech of each word in a sentence, while sound signal identification is like a social network graph where the model decides the characteristics of each node.
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10:09
Sentiment Analysis helps machines interpret positive or negative comments, while machine learning outputs include voice recognition and sequence labeling.
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13:29
Fully-Connected Networks struggle with contextual information, but using a window of the first and last five frames can give good results.
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16:41
Google's Transformer network architecture uses Self-Attention technology to process entire sequence information efficiently.
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19:14
The lecture explains how to use self-attention mechanism to calculate the correlation between vectors in a sequence and generate a row of vector b.
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22:41
The lecture explains how Attention is calculated using the Transformer method, which involves calculating correlations between Query and Key vectors to obtain a score called Attention.