88. What is GloVe, and how does it differ from Word2Vec?

GloVe Global Vectors for Word Representation is a word embedding technique developed by Stanford University.

Difference: 

• Word2Vec: Learns embeddings using local context within a sliding window 

• GloVe: Learns embeddings using global word co-occurrence statistics from the entire corpus 

89. What is BERT, and how does it work?

BERT Bidirectional Encoder Representations from Transformers is a Transformer-based language model developed by OpenAI.

Unlike earlier models, BERT reads text in both directions left-to-right and right-to-left, allowing it to understand context more effectively.

Applications: Question answering, Text classification, Named Entity Recognition NER, Sentiment analysis, Search engines 

90. What is GPT, and how is it different from BERT?

BERT 

• Bidirectional 

• Encoder-only architecture 

• Best for language understanding tasks 

• Examples: Classification, search, NER 

GPT 

• Unidirectional autoregressive 

• Decoder-only architecture 

• Best for text generation 

• Examples: Chatbots, content generation, coding assistants, summarization 

Both are foundation models but are optimized for different types of NLP tasks.

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