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Transformers Architecture Theory

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Transformers are the foundation of modern AI (GPT, BERT, vision models). Understanding attention, positional encoding, and scaling laws is essential for AI engineers. Salary: $110-170k junior, $200-300k mid, $350-500k senior. Learn in 6-8 weeks. Adjacent to deep learning, NLP, and machine learning.

Transformers Architecture Theory maali?

Transformers are a deep learning architecture based on attention mechanisms, introduced in "Attention Is All You Need" (Vaswani et al., 2017). They revolutionized NLP by enabling parallel processing of sequences, longer context, and better representation learning. Modern language models (GPT, BERT, Claude) are all Transformers. Core concepts: self-attention (tokens attend to each other), multi-head attention (multiple attention patterns), feedforward networks, positional encodings, and scaling laws that govern model performance.

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Why are Transformers better than RNNs?
Transformers process all tokens in parallel (faster training), have longer context windows (better memory), and learn better representations via attention. RNNs are sequential (slower) and suffer from vanishing gradients.
What's attention?
Attention lets each token focus on other relevant tokens. Query-Key-Value attention computes weighted importance of each token to every other token, enabling the model to ignore irrelevant context.
What are positional encodings?
Transformers don't inherently know token order (they process in parallel). Positional encodings add position information to embeddings. Absolute (fixed) or relative (learned) encodings both work.
How do Transformers scale?
Scaling laws show that loss decreases predictably with model size and data. Larger models (GPT-4) trained on more data (terabytes) achieve better results. Scaling follows power laws.
What's the computational cost?
Attention is O(n²) in sequence length (expensive for long sequences). Modern variants (FlashAttention, sparse attention) reduce this. Training large models requires $100k-$10M in compute.

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