|
gptkbp:instanceOf
|
gptkb:university
|
|
gptkbp:assignmentType
|
programming assignments
final project
written assignments
|
|
gptkbp:course
|
gptkb:CS231N
|
|
gptkbp:department
|
gptkb:Stanford_Computer_Science_Department
|
|
gptkbp:firstTaught
|
2015
|
|
gptkbp:focusesOn
|
computer vision
convolutional neural networks
deep learning
|
|
gptkbp:fullName
|
gptkb:Convolutional_Neural_Networks_for_Visual_Recognition
|
|
gptkbp:hasTerm
|
Spring Quarter
|
|
gptkbp:hasTextbook
|
no official textbook
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
CS231N
|
|
gptkbp:language
|
English
|
|
gptkbp:level
|
graduate
advanced undergraduate
|
|
gptkbp:location
|
gptkb:Stanford,_California
|
|
gptkbp:notableAlumni
|
gptkb:Andrej_Karpathy
|
|
gptkbp:notableFaculty
|
gptkb:Serena_Yeung
gptkb:Andrej_Karpathy
gptkb:Fei-Fei_Li
gptkb:Justin_Johnson
|
|
gptkbp:offeredBy
|
gptkb:Stanford_University
|
|
gptkbp:openCourseware
|
yes
|
|
gptkbp:platform
|
gptkb:TensorFlow
gptkb:PyTorch
|
|
gptkbp:prerequisite
|
probability
linear algebra
Python programming
machine learning basics
|
|
gptkbp:syllabusTopic
|
optimization
generative models
recurrent neural networks
transfer learning
image classification
object detection
training neural networks
deep learning frameworks
visual recognition
detection and segmentation
practical tips for deep learning
visualizing and understanding CNNs
|
|
gptkbp:targetAudience
|
students interested in deep learning and computer vision
|
|
gptkbp:teaches
|
gptkb:concert_tour
projects
assignments
|
|
gptkbp:videoLecturesAvailable
|
gptkb:YouTube
|
|
gptkbp:website
|
http://cs231n.stanford.edu/
|
|
gptkbp:bfsParent
|
gptkb:Serena_Yeung
|
|
gptkbp:bfsLayer
|
6
|