|
gptkbp:instanceOf
|
gptkb:university
|
|
gptkbp:course
|
gptkb:CS231N
|
|
gptkbp:courseWebsite
|
http://cs231n.stanford.edu/
|
|
gptkbp:department
|
gptkb:Computer_Science_Department
|
|
gptkbp:focusesOn
|
computer vision
convolutional neural networks
deep learning
|
|
gptkbp:fullTitle
|
CS231N: Convolutional Neural Networks for Visual Recognition
|
|
gptkbp:hasAssignments
|
Yes
|
|
gptkbp:hasFinalProject
|
Yes
|
|
gptkbp:hasOnlineLectures
|
Yes
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Stanford CS231N course
|
|
gptkbp:languageOfInstruction
|
English
|
|
gptkbp:lectureVideosAvailableOn
|
gptkb:YouTube
|
|
gptkbp:mainInstructor
|
gptkb:Serena_Yeung
gptkb:Fei-Fei_Li
gptkb:Justin_Johnson
|
|
gptkbp:notableAlumni
|
gptkb:Andrej_Karpathy
|
|
gptkbp:offeredBy
|
gptkb:Stanford_University
2015
|
|
gptkbp:openToPublic
|
Yes
|
|
gptkbp:prerequisite
|
probability
linear algebra
Python programming
|
|
gptkbp:previousInstructor
|
gptkb:Andrej_Karpathy
|
|
gptkbp:targetAudience
|
graduate students
advanced undergraduates
|
|
gptkbp:teaches
|
recurrent neural networks
transfer learning
image classification
object detection
training neural networks
visual recognition
visualizing and understanding CNNs
deep learning architectures
|
|
gptkbp:bfsParent
|
gptkb:Tiny_ImageNet
|
|
gptkbp:bfsLayer
|
7
|