ES 667 · Deep Learning

IIT Gandhinagar · Semester I, 2026–27 · Prof. Nipun Batra

This site contains the slides, notebooks, assignments, and interactive material for ES 667. The dated class record, grading, and announcements are on the 2026 course site.

Current class

Generalization & Regularization

• ▤ Slides · ▶ Presentation mode
• ★ Two-page cheatsheet
• ⌨ Learning curves, overfitting & early stopping
• ⌨ Dropout and regularization mechanisms

Recently taught

• Valid Outputs & Learning-Rate Schedules — constrained parameters, warmup, decay, and cosine schedules. ▤ Valid outputs · ▤ LR schedules · ★ Cheatsheet
• Vector, Matrix & Affine Autograd — shape-safe reverse mode through dense layers and batches. ▤ Calculus · ▤ Autograd · ★ Cheatsheet · ⌨ Colab
• Backpropagation & Autograd from Scratch — computation graphs, reverse mode, and a scalar engine. ▤ Slides · ★ Cheatsheet · ⌨ Colab
• Optimization for Deep Learning — gradient estimates through momentum, RMSProp, and Adam. ▤ Slides · ★ Optimization I · ★ Optimization II

Course materials

• Lecture decks
• Notebooks
• Assignments
• Course guide
• References

Questions and announcements are handled in the course Slack workspace.