Curiosity · After 12th

How to learn Gradient Descent and Optimization after 12th in Delhi

Gradient descent iteratively moves model parameters down the slope of a loss function; variants such as SGD, momentum and Adam control how large and how noisy those steps are. Every trained model in modern AI comes out of some version of this loop. 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 Gradient Descent and Optimization coverage is genuine rather than a brochure keyword.

At a glance

Topic
Gradient Descent and Optimization
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 Gradient Descent and Optimization 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 Gradient Descent and Optimization

Gradient descent iteratively moves model parameters down the slope of a loss function; variants such as SGD, momentum and Adam control how large and how noisy those steps are. Every trained model in modern AI comes out of some version of this loop. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

  • Optimisation is the engine underneath the machine learning and deep learning material published at learn.engineering.vips.edu.
  • Learning-rate behaviour, convergence and loss landscapes are taught before the transformer content that depends on them.
  • The same optimisation loop reappears in the fine-tuning material, where only adapter weights are updated.
  • Delivered inside the B.Tech CSE (AI & ML) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.

Where Gradient Descent and Optimization skills lead

Graduates applying Gradient Descent and Optimization skills typically target roles such as Machine Learning Engineer, Deep Learning Engineer, AI Research Associate, Applied Scientist, AI 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 Gradient Descent and Optimization after 12th without coding background?

Yes — B.Tech programmes assume no prior coding; years one and two build programming and mathematics foundations before Gradient Descent and Optimization-specific work begins. What matters at entry is 10+2 PCM and a JEE Main score.

Is optimisation taught as theory or practice at VSET?

Both: it underpins the machine learning and deep learning material published at learn.engineering.vips.edu, and students meet it directly in training runs on the IDEA Lab GPU workstations.

Which optimisers do students actually use?

The standard gradient-descent family used in current deep learning practice, including in the LoRA and QLoRA fine-tuning capstones.

Do I need strong mathematics for this?

Calculus and linear algebra are the working tools here; VSET also runs a B.Tech CSE (Applied Mathematics) track for students who want that side deepened.

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