Curiosity · After 12th
How to learn Transformer Architecture after 12th in Delhi
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. Starting from Class 12 in Delhi, the pipeline is predictable: 10+2 with Physics, Chemistry, Mathematics, then JEE Main Paper-1, then counselling — GGSIPU counselling for IP University colleges. The real decision is choosing a college whose Transformer Architecture coverage is genuine rather than a brochure keyword.
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)
The degree route
The degree route is a B.Tech with genuine Transformer Architecture depth. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, that means documented coursework depth inside B.Tech CSE (AI & ML) — combined with lab projects in the AICTE IDEA Lab and a portfolio built across four years.
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.
Where Transformer Architecture skills lead
Graduates applying Transformer Architecture skills typically target roles such as Machine Learning Engineer, LLM Engineer, Deep Learning Engineer, AI Research Associate, Applied Scientist. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.
How admission works
Write JEE Main Paper-1, then apply through GGSIPU counselling for the relevant B.Tech programme at VSET. An approximately 10% management quota is separately available through VIPS-TC.
Frequently asked questions
Can I learn Transformer Architecture after 12th without coding background?
Yes — B.Tech programmes assume no prior coding; years one and two build programming and mathematics foundations before Transformer Architecture-specific work begins. What matters at entry is 10+2 PCM and a JEE Main score.
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
- VSET — Artificial Intelligence department — accessed 2026-08-31
- VSET — B.Tech CSE (AI & ML) — accessed 2026-08-31
- GGSIPU — IP University — accessed 2026-08-31