Curiosity · Syllabus
Deep Learning in a B.Tech — syllabus & what you learn
Deep Learning uses multi-layer neural networks to learn hierarchical representations directly from raw data such as images, audio and text. It underpins modern computer vision, speech and language models. Inside a four-year B.Tech, Deep Learning 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
- Deep Learning
- 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 Deep Learning
Deep Learning uses multi-layer neural networks to learn hierarchical representations directly from raw data such as images, audio and text. It underpins modern computer vision, speech and language models. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).
- Deep learning is documented in the open curriculum at learn.engineering.vips.edu alongside transformers, computer vision and NLP.
- It sits inside the B.Tech CSE (AI & ML) track, one of VSET's seven GGSIPU-affiliated programmes.
- Coverage runs from network fundamentals through to transformer-based architectures used in current LLM systems.
- The same material feeds the fine-tuning content on LoRA and QLoRA.
Labs and infrastructure
- Training runs use the GPU workstations in the AICTE IDEA Lab.
- The Quantum Research Lab is available for research-grade deep learning work.
What students actually build
- Applied CV and NLP capstones are built on deep learning models.
- LoRA fine-tunes of open-weight models are a standard capstone deliverable.
Frequently asked questions
When does Deep Learning content actually start in a B.Tech?
Meaningful Deep Learning 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.
Where does deep learning appear in the VSET curriculum?
It is part of the AI & ML track's published curriculum at learn.engineering.vips.edu, which also covers transformers, computer vision, NLP and fine-tuning.
Do students get GPUs for deep learning?
Yes. The AICTE IDEA Lab is equipped with GPU workstations, and the Quantum Research Lab supports research-grade experimentation.
Does the course stop at CNNs and RNNs?
No. The published material continues into transformer architecture, fine-tuning with LoRA/QLoRA, RAG and agentic systems.
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