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๐Ÿง  Topic 25: BERT โ€” Bidirectional Encoder Representations from Transformers

Course: NLP Advanced | Section 4: Pre-trained Language Models Topic: BERT โ€” The Model That Changedโ€ฆ

Topic 1
โœจ

๐Ÿ“Œ BERT Kya Hai?

๐Ÿ“š BERT = Bidirectional Encoder Representations from Transformers Google ne 2018 me releaseโ€ฆ
Topic 2
๐Ÿ’ก ๐Ÿ“Š ๐Ÿ”ฌ

๐Ÿ—๏ธ BERT Architecture โ€” Complete Deep Dive

๐Ÿ’ก Core Architecture ` BERT Architecture (Encoder-Only Transformer): Input: "The cat sat onโ€ฆ
Topic 3
๐Ÿ“ฅ โš™๏ธ ๐Ÿ”ฌ ๐Ÿ’ก

๐ŸŽฏ BERT Pre-training โ€” 2 Tasks

๐ŸŽฏ Task 1: Masked Language Model (MLM) ` MLM โ€” BERT Ka Core Innovation: Original: "The catโ€ฆ
Topic 4
โญ

๐Ÿ”ง BERT Fine-Tuning โ€” Task-Specific

โญ ` Fine-Tuning Architecture per Task: 1๏ธโƒฃ CLASSIFICATION (Sentiment, Intent): [CLS] โ†’โ€ฆ
Topic 5
โœจ

๐Ÿ’ป Complete Python Implementation

๐Ÿ’ก

Frequent words: "the" โ†’ ["the"]

๐Ÿ”‘

Rare words: "embeddings" โ†’ ["em",โ€ฆ

โšก

"##" prefix = continuation ofโ€ฆ

๐ŸŽฏ

Koi masking nahi (unlike GPT'sโ€ฆ

Topic 6
โœจ ๐Ÿ“Š ๐Ÿ”ฌ

๐Ÿ”‘ Key BERT Innovations โ€” Summary

โœจ ` BERT ki 5 Game-Changing Innovations:โ€ฆ
Topic 7
๐Ÿ”’

โ“ Quiz โ€” BERT Mastery Check

๐Ÿ’ก

A) Left-to-Right + Right-to-Leftโ€ฆ

๐Ÿ”‘

B) Har layer me har token donoโ€ฆ

โšก

C) Two separate models train kartaโ€ฆ

๐ŸŽฏ

D) Positional encoding seโ€ฆ

Topic 8
๐Ÿš€

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๐Ÿง  Topic 25: BERT โ€” Bidirectional Encoder Representations from Transformers ka sabse sahi definition kya hai?

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๐Ÿง  Topic 25: BERT โ€” Bidirectional Encoder Representations from Transformers Complete!

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๐Ÿง  Topic 25: BERT โ€” Bidirectional Encoder Representations from Transformers Complete

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