Transformers & NLP

Master the Transformer architecture and modern NLP from attention to LLMs. Explore the architecture that revolutionized AI.

Level: Advanced · Category: NLP · Estimated time: 7 hours

Prerequisites

Word Embeddings & Tokenization — Word2Vec, GloVe, subword tokenization (BPE, WordPiece, SentencePiece).
Self-Attention Mechanism — Query, Key, Value — how self-attention computes relationships between tokens.
The Transformer Architecture — Multi-head attention, positional encoding, encoder-decoder, feed-forward layers.
BERT & Encoder Models — Masked language modeling, NSP, and bidirectional encoding.
GPT & Decoder Models — Autoregressive generation, causal attention, and the GPT family.
T5, BART & Seq2Seq Models — Encoder-decoder transformers for translation, summarization, and generation.
Fine-Tuning with Hugging Face — Using the Transformers library, Trainer API, and custom fine-tuning.
Prompt Engineering & In-Context Learning — Zero-shot, few-shot, chain-of-thought, and effective prompt design.
RLHF & Alignment — Reinforcement learning from human feedback, reward modeling, and AI alignment.

transformers, nlp, attention, bert, gpt, huggingface, llm, fine-tuning