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AI theory, architecture, models
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Module 12
Foundations of Language Modeling and Embeddings
1
Word2Vec with Negative Sampling
Train word embeddings using the Word2Vec (Skip-gram) model with negative sampling
2
Tokenization Techniques: BPE and WordPiece
Apply tokenization techniques, including Byte-Pair Encoding (BPE) and WordPiece
3
Understanding BERT's Masked Language Model (MLM)
Explain the masked language modeling (MLM) objective used by BERT
4
Understanding Autoregressive Language Modeling in GPT
Explain the causal (autoregressive) language modeling objective used by GPT
5
Fine-tuning LLMs for Text Classification
Fine-tune a pre-trained language model for a downstream text classification task
6
Transfer Learning in NLP: Principles and Benefits
Understand the principles and benefits of transfer learning in NLP
Previous module
The Transformer Architecture
Next module
Modern Language Model Architectures