Gautam AI

Transformers & Attention

Gautam AI presents a focused program on Transformers and Attention mechanisms, the core technologies behind modern NLP, AI copilots, and Large Language Models (LLMs).

Attention Mechanisms Transformer Architecture NLP to LLM Bridge Modern AI Foundations

What Are Transformers & Attention?

Transformers are neural architectures built around attention mechanisms, allowing models to focus on the most relevant parts of input data.

This approach replaced traditional sequence models and enabled scalable, parallel training for modern NLP and Large Language Models.

Key Concepts Covered

Self-Attention
Query, key, value interactions.

Multi-Head Attention
Parallel attention subspaces.

Positional Encoding
Injecting order into sequences.

Encoder–Decoder
Translation & sequence modeling.

Scaling & Efficiency
Depth, width, and attention costs.

Transformer Variants
BERT, GPT-style architectures.

Who Should Learn This?

• Learners transitioning from NLP to LLMs
• AI engineers building language models
• Researchers exploring modern architectures
• Professionals working on AI copilots & agents

What Comes After Transformers?

• Large Language Models (LLMs)
• Fine-tuning & instruction tuning
• Retrieval-Augmented Generation (RAG)
• β-level professional NLP & LLM systems

· Transformers & Attention · NLP & LLM Foundations · Gautam AI International Pvt. Ltd.