Recurrent Neural Networks internships for B.Tech students in Delhi

What students actually build

• Sequence models appear in applied NLP and time-series capstones.
• Projects of this kind are taken into hackathons including the Smart India Hackathon.

How VSET teaches Recurrent Neural Networks

Recurrent neural networks carry a hidden state across a sequence so earlier inputs influence later outputs; LSTM and GRU cells add gating so that signal survives long spans. They were the dominant sequence architecture before attention replaced them. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• RNNs, LSTMs and GRUs are part of the deep learning and NLP material published at learn.engineering.vips.edu.
• The curriculum teaches them as the direct precursor to attention and transformer architecture, which it also covers.
• Their failure modes — vanishing gradients over long sequences — are the motivation the transformer content builds on.
• 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 Recurrent Neural Networks skills lead

Graduates applying Recurrent Neural Networks skills typically target roles such as NLP Engineer, Deep Learning Engineer, Machine Learning Engineer, Data 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.