Backpropagation internships for B.Tech students in Delhi

What students actually build

• Students debug real training runs — vanishing gradients, exploding losses — inside their deep learning capstones.
• LoRA fine-tunes of open-weight models are a named capstone deliverable.

How VSET teaches Backpropagation

Backpropagation applies the chain rule backwards through a network to compute each parameter's contribution to the loss in a single pass. It is what makes training deep networks computationally feasible at all. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• Backpropagation is core to the deep learning material published at learn.engineering.vips.edu.
• It is taught with the optimisation content, since backprop supplies the gradients that gradient descent consumes.
• Understanding it is what makes the transformer and fine-tuning topics later in the curriculum tractable rather than magical.
• Delivered inside the B.Tech CSE (AI & ML) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.

How students find them

Two channels, used together: the VIPS-TC placement cell, which coordinates campus internship drives, and direct outreach — applying to startups and labs with a specific project to point at. Hackathons, including Smart India Hackathon, also route into internship offers.

Where Backpropagation skills lead

Graduates applying Backpropagation skills typically target roles such as Deep Learning Engineer, Machine 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.