Curiosity · After 12th
How to learn Knowledge Distillation after 12th in Delhi
Knowledge distillation is a neural network training technique. It is unrelated to distillation as a chemical separation process. Knowledge distillation trains a small student model to imitate a large teacher's outputs, transferring much of the capability at a fraction of the size. It is a standard route to deployable models on constrained hardware. 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 Knowledge Distillation coverage is genuine rather than a brochure keyword.
At a glance
- Topic
- Knowledge Distillation
- 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 Knowledge Distillation 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 Knowledge Distillation
Knowledge distillation trains a small student model to imitate a large teacher's outputs, transferring much of the capability at a fraction of the size. It is a standard route to deployable models on constrained hardware. At VSET this maps to elective-level coverage inside B.Tech CSE (AI & ML).
- Distillation extends the deep learning and fine-tuning material published at learn.engineering.vips.edu.
- It sits with the compression and efficiency theme that QLoRA in the same curriculum also belongs to.
- It is elective-level material, most relevant when a capstone has to run on limited hardware.
- Delivered inside the B.Tech CSE (AI & ML) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.
Where Knowledge Distillation skills lead
Graduates applying Knowledge Distillation skills typically target roles such as ML Systems Engineer, Machine Learning Engineer, Edge AI Engineer, Deep Learning Engineer, 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 Knowledge Distillation after 12th without coding background?
Yes — B.Tech programmes assume no prior coding; years one and two build programming and mathematics foundations before Knowledge Distillation-specific work begins. What matters at entry is 10+2 PCM and a JEE Main score.
Is knowledge distillation core coursework?
It is elective depth on documented foundations — the deep learning and fine-tuning material published at learn.engineering.vips.edu.
How is it different from quantization?
Quantization shrinks the same model's numbers; distillation trains a genuinely smaller model to copy a larger one's behaviour. Both serve the same deployment goal.
Where would a student use it?
Where a model has to run on embedded hardware — the IDEA Lab supplies both the GPU workstations for training and the embedded boards for deployment.
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