Deep Learning

IIT Gandhinagar · Semester I, 2026–27

ES 667 develops deep-learning models from their statistical and computational foundations.

Instructor Prof. Nipun Batra Office 13/401C Credits 3-0-0-4 nipun.batra@iitgn.ac.in

Lecture libraryNotes, slides, cheat sheets, notebooks and videos

Assignments

Assignment 1 · OptimizationAssignment 2 · Small models and attention

Assessment

Course policies and submission dates

GradingDeadlines

Course help

Questions, announcements and discussion

Course FAQSlack workspace

Course overview

The course begins with likelihood, loss functions and multilayer networks, and proceeds to convolutional models, sequence models, Transformers, representation learning and generative models.

References

There is no required textbook. Useful references include:

• Simon J. D. Prince, Understanding Deep Learning (online edition)
• Christopher M. Bishop and Hugh Bishop, Deep Learning: Foundations and Concepts
• Aston Zhang, Zachary C. Lipton, Mu Li and Alexander J. Smola, Dive into Deep Learning (online edition)