Deep Learning
BERT — How Google Changed Search with Bidirectional Understanding
Understand how BERT revolutionized NLP by processing text bidirectionally for better comprehension.
- Bidirectional context — understand words from both directions
- Pre-training + fine-tuning — powerful transfer learning paradigm
- Search and Q and A — transformed Google Search and beyond
Understanding context is the key to understanding language.
BERT and Encoder Models — Complete Guide
BERT (Bidirectional Encoder Representations from Transformers, Devlin et al., 2018) revolutionized NLP by introducing bidirectional pre-training on large unlabeled text, followed by task-specific fine-tuning. It demonstrated that pre-training + fine-tuning outperforms training from scratch on nearly every NLP benchmark.
BERT Architecture
Pre-training Objectives
Fine-tuning BERT
BERT for Different Tasks
Fine-Tuning BERT
Key Takeaways
What to Learn Next
-> GPT Architecture Compare with autoregressive models.
-> Transformers Master the underlying architecture.
-> NLP Fundamentals Learn natural language processing basics.
-> Transfer Learning Apply pre-trained models to new tasks.
-> Pre-training Language Models Understand how models learn from text.
-> Tokenization for LLMs Learn how text is converted to tokens.