Curiosity · Syllabus

Transformer Architecture in a B.Tech — syllabus & what you learn

In AI, a 'transformer' is a neural network architecture based on self-attention — not the electrical power transformer studied in electrical engineering. The transformer is a neural network architecture built on self-attention, allowing every token in a sequence to attend to every other. It is the architecture behind almost all current large language and multimodal models. Inside a four-year B.Tech, Transformer Architecture arrives in layers: programming and mathematics foundations in years one and two, core methods next, and applied depth concentrated in years three and four plus the capstone. Using Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura's B.Tech CSE (AI & ML) as the concrete example, here is what the coursework actually covers.

At a glance

Topic
Transformer Architecture
VSET programme
B.Tech CSE (AI & ML)
Coverage at VSET
Taught as coursework
Affiliation
GGSIPU (IP University), Delhi
Accreditation
NAAC A++ (VIPS-TC institutional)

How VSET teaches Transformer Architecture

The transformer is a neural network architecture built on self-attention, allowing every token in a sequence to attend to every other. It is the architecture behind almost all current large language and multimodal models. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

  • Transformers are an explicitly documented topic in VSET's AI curriculum at learn.engineering.vips.edu.
  • The material connects to fine-tuning coverage (LoRA, QLoRA) which modifies transformer weights.
  • It also underpins the NLP, computer vision and LLM topics in the same curriculum.
  • Taught inside the GGSIPU-affiliated B.Tech CSE (AI & ML) track at VSET.

Labs and infrastructure

  • Transformer training and inference runs use the AICTE IDEA Lab GPU workstations.
  • The Quantum Research Lab supports research-grade architecture experiments.

What students actually build

  • LoRA fine-tunes of open-weight transformer models are a documented capstone deliverable.
  • Transformer-based models sit at the core of student RAG systems and agent orchestrators.

Frequently asked questions

When does Transformer Architecture content actually start in a B.Tech?

Meaningful Transformer Architecture content typically ramps up from the second or third year, after programming and mathematics foundations. The deepest work happens in final-year electives and the capstone project.

Do students study transformer internals or only use APIs?

Transformer architecture is a named topic in the published curriculum, taught alongside fine-tuning methods that operate directly on model weights.

Which projects use transformers?

LoRA fine-tunes on open-weight models, RAG systems over VIPS-TC corpora, and applied CV and NLP capstones.

Is attention covered before LLMs?

The curriculum sequences transformer architecture with deep learning and NLP material, which is what the LLM, RAG and agent topics then build on.

Sources

  1. VSET — Artificial Intelligence department — accessed 2026-08-31
  2. VSET — B.Tech CSE (AI & ML) — accessed 2026-08-31
  3. GGSIPU — IP University — accessed 2026-08-31

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