BERT Explained: How AI Understands Context (Bidirectional Models)
Автор: CodeLucky
Загружено: 2026-01-14
Просмотров: 4
Ever wondered how computers truly understand the nuances and multiple meanings of words based on their surrounding context? 🤖 In this comprehensive video, we break down BERT (Bidirectional Encoder Representations from Transformers), the revolutionary open-source model developed by Google that completely changed the landscape of Natural Language Processing forever.
Unlike traditional unidirectional models that read text sequentially from left to right (or right to left), BERT reads the entire sentence at once in both directions! ↔️ We will explore the fundamental differences between Unidirectional and Bidirectional models, dive deep into the Transformer Encoder architecture, and explain clever pre-training methods like Masked Language Modeling (MLM) and Next Sentence Prediction (NSP).
Whether you are a computer science student, a software developer, or just an enthusiast curious about the future of AI, this guide makes these complex concepts simple and accessible. 📚 Learn how BERT powers search engines and improves translation accuracy.
Topics covered in this video:
The importance of Context in Language
Encoder vs Decoder Architecture explained
How Masked Language Modeling works
Techniques for Fine-Tuning BERT for custom NLP tasks
#GenerativeAI #BERT #MachineLearning #NLP #ArtificialIntelligence #DeepLearning #TechEducation
Chapters:
00:00 - BERT and Bidirectional Models
00:17 - Why Context Matters in Language
00:41 - Unidirectional vs. Bidirectional
01:07 - What is BERT?
01:26 - The Architecture: Encoder Only
01:48 - Training Task 1: Masked Language Modeling (MLM)
02:09 - Training Task 2: Next Sentence Prediction (NSP)
02:31 - The Magic of Fine-Tuning
02:54 - Summary: Why BERT Changed Everything
03:16 - Outro
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