printer friendly pageCS 231N: Convolutional Neural Networks for Visual Recognition
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks. Prerequisites: Proficiency in Python; CS131 and CS229 or equivalents; MATH21 or equivalent, linear algebra.
Terms: Spr
| Units: 3-4
Instructors:
Li, F. (PI)
MATH 21: Calculus
Review of limit rules. Sequences, functions, limits at infinity, and comparison of growth of functions. Review of integration rules, integrating rational functions, and improper integrals. Infinite series, special examples, convergence and divergence tests (limit comparison and alternating series tests). Power series and interval of convergence, Taylor polynomials, Taylor series and applications. Prerequisite:
Math 20 or equivalent. If you have not previously taken a calculus course at Stanford then you must have taken the math placement diagnostic (offered through the Math Department website) in order to register for this course.
Terms: Aut, Win, Spr, Sum
| Units: 4
| UG Reqs: GER:DB-Math, WAY-FR
Instructors:
Kim, G. (PI)
;
Lee, S. (PI)
;
Ma, C. (PI)
;
Wieczorek, W. (PI)
;
Chaturvedi, S. (TA)
;
Dore, D. (TA)
;
Xu, M. (TA)
MATH 21A: Calculus, ACE
Students attend one of the regular
MATH 21 lectures with a longer discussion section of two hours per week instead of one. Active mode: students in small groups discuss and work on problems, with a TA providing guidance and answering questions. Application required:
https://forms.gle/BZJqJTawa5PUqe9E7.
Terms: Aut, Win, Spr
| Units: 5
| UG Reqs: WAY-FR
Instructors:
Kim, G. (PI)
;
Nuti, P. (TA)
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