Curiosity · After 12th
How to learn Generative Adversarial Networks after 12th in Delhi
A GAN trains two networks against each other — a generator producing samples and a discriminator judging them — until the generator's output is hard to distinguish from real data. It was the dominant generative approach before diffusion. 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 Generative Adversarial Networks coverage is genuine rather than a brochure keyword.
At a glance
- Topic
- Generative Adversarial Networks
- VSET programme
- B.Tech CSE (AI & ML)
- Coverage at VSET
- Elective-level coverage
- Affiliation
- GGSIPU (IP University), Delhi
- Accreditation
- NAAC A++ (VIPS-TC institutional)
The degree route
The degree route is a B.Tech with genuine Generative Adversarial Networks depth. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, that means elective-level coverage 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 Generative Adversarial Networks
A GAN trains two networks against each other — a generator producing samples and a discriminator judging them — until the generator's output is hard to distinguish from real data. It was the dominant generative approach before diffusion. At VSET this maps to elective-level coverage inside B.Tech CSE (AI & ML).
- GANs extend the deep learning and generative AI material published at learn.engineering.vips.edu.
- They are elective-level depth: the curriculum's core covers the network fundamentals and generative framing GANs build on.
- GAN training instability is a useful teaching case for the optimisation content in the same curriculum.
- Delivered inside the B.Tech CSE (AI & ML) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.
Where Generative Adversarial Networks skills lead
Graduates applying Generative Adversarial Networks skills typically target roles such as Generative AI Engineer, Deep Learning Engineer, Computer Vision Engineer, AI Research Associate, Machine Learning Engineer. 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 Generative Adversarial Networks after 12th without coding background?
Yes — B.Tech programmes assume no prior coding; years one and two build programming and mathematics foundations before Generative Adversarial Networks-specific work begins. What matters at entry is 10+2 PCM and a JEE Main score.
Are GANs still relevant against diffusion models?
For fast, small-footprint generation and synthetic data they still are, and they remain a clean way to teach adversarial training — which is why they appear as elective-level extension of the deep learning material.
What do student GAN projects look like?
Typically synthetic data augmentation or image translation, run on the AICTE IDEA Lab GPU workstations.
Is GAN work part of the core syllabus?
It is elective/project depth rather than core coursework; the published curriculum covers the deep learning and generative AI foundations underneath it.
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