Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Deep Learning

Massachusetts Institute of Technology via MIT OpenCourseWare

Go to class Write review

Simplilearn

Pass the PMP® Exam on Your First Try — Expert-Led Training

GetSmarter

MIT Sloan: Lead AI Adoption Across Your Organization — Not Just Pilot It

Overview

Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off

One annual plan covers every course and certificate on Coursera. 40% off for a limited time.

Get Full Access

This course covers the fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high dimensions, and applications to computer vision, natural language processing, and robotics.

Syllabus

Lec 01. Introduction to Deep Learning
Lec 02. How to Train a Neural Net
Lec 03. Approximation Theory
Lec 04. Architectures: Grids
Lec 05. Architectures: Graphs
Lec 06. Generalization Theory
Lec 07. Scaling Rules for Optimization
Lec 08. Architectures: Transformers
Lec 09. Hacker's Guide to Deep Learning
Lec 10. Architectures: Memory
Lec 11. Representation Learning: Reconstruction-Based
Lec 12. Representation Learning: Similarity-Based
Lec 13. Representation Learning: Theory
Lec 14. Generative Models: Basics
Lec 15. Generative Models: Representation Learning Meets Generative Modeling
Lec 16. Generative Models: Conditional Models
Lec 17. Generalization: Out-of-Distribution (OOD)
Lec 18. Transfer Learning: Models
Lec 19. Transfer Learning: Data
Lec 20. Scaling Laws
Lec 21. Language Models
Lec 23. Metrized Deep Learning
Lec 24. Inference Methods for Deep Learning
PyTorch Tutorial

Taught by

Prof. Sara Beery, Dr. Jeremy Bernstein, and Prof. Phillip Isola

Tags

united states

Reviews

Start your review of Deep Learning