CSC413H1
UTSGNeural Networks and Deep Learning
An introduction to neural networks and deep learning. Backpropagation and automatic differentiation. Architectures: convolutional networks and recurrent neural networks. Methods for improving optimization and generalization. Neural networks for unsupervised and reinforcement learning.
View full details on the UofT Academic CalendarPrereq: CSC311H1/ CSC311H5/ CSCC11H3/ STA314H1; MAT235H1/ MAT235Y1/ MAT237Y1/ MAT257Y1/ MAT257Y5/ MAT232H5/ MAT233H5/ MATB41H3; MAT223H1/ MAT240H1/ MAT223H5/ MATA23H3 Prerequisite for Faculty of Applied Science and Engineering students: ECE421H1/ ROB313H1; MAT291H1/ MAT294H1/ AER210H1/ MIE230H1; MAT185H1/ MAT188H1Breadth: Physical & Mathematical UniversesExcl: CSC321H5, CSC413H5. NOTE: Students not enrolled in the Computer Science Major or Specialist program at A&S, UTM, or UTSC, or the Data Science Specialist at A&S, are limited to a maximum of 1.5 credits in 300-/400-level CSC/ECE courses.
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Course Info
DepartmentCSC
CampusUTSG (St. George)
Level400
Hours24L/12T
BreadthPhysical & Mathematical Universes
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From Reddit
2021 SummerDifficulty: 4/5
I also thought csc311 had a really heavy workload. Did no one else think csc413 had an even heavier workload? Maybe it was just bad for me because I took it last semester online and we had 7 long assignments, a midterm and a final project
View on Reddit1 months ago