Calendar

Current Running Schedule

Week 1

Sept 04

Lec 1Course Introduction: 📖 GBC Ch. 1

Week 2

Sept 09

Lec 2Image Classification: 📖 231n Image Classification

Sept 11

Lec 3Linear Classifiers: 📖 Chen et al., ProgressLabeller

Week 3

Sept 16

Lec 4Regularization and Optimization: 📖 231n Optimization

: 📖 GBC Sec 8.1-8.6

: 📖 Li et al., Visualizing Loss Landscaps

: 📖 Goh, Why Momentum Really Works

Sept 18

Lec 5Neural Networks: 📖 231n Neural Networks

: 📖 GBC Sec 6.1-6.4

: 📖 Nielsen, Universal Approximation

Week 4

Sept 23

Lec 6Backpropagation: 📖 cs231n Backpropagation

: 📖 GBC Sec 6.5-6.6

: 📖 Olah, Backpropagation

Sept 25

Lec 7CNNs I: 📖 cs231n ConvNets

: 📖 GBC Ch. 9

: 📖 Araujo et al., Receptive Fields of CNNs

: 📖 Olah et al., Feature Visualization

Week 5

Sept 30

Lec 8CNNs II: 📖 Krizhevsky et al., AlexNet

: 📖 Simonyan and Zisserman, VGG

: 📖 Szegedy et al., GoogLeNet

: 📖 He et al., ResNet

Oct 02

Lec 9Training NNs I: 📖 cs231n Training I

: 📖 Olah et al., Equivariance in Networks

Week 6

Oct 07

Lec 10Training NNs II: 📖 cs231n Training II

: 📖 Goodfellow et al., Adversarial Examples

: 📖 Deep Learning Software Slides

: 📖 MSI Notes; Recording

: 📖 Redmon, Darknet

: 📖 Jia et al., Caffe

: 📖 Abadi et al., TensorFlow

: 📖 Paszke et al., PyTorch

Oct 09

Lec 11Object Detection: 📖 Girshick et al., R-CNN

: 📖 Redmon et al., YOLO

: 📖 Lin et al., FCOS

Week 7

Oct 14

Lec 12Object Detection and Segmentation: 📖 Girshick et al., R-CNN

: 📖 Redmon et al., YOLO

: 📖 Lin et al., FCOS

Oct 16

Lec 13Robot Grasp Learning: 📖 Platt et al., Grasp Learning

: 📖 Reading list

Week 8

Oct 21: Lec 14Imitation Learning I
Oct 22: Lec 15Imitation Learning II

Week 9

Oct 28: Lec 16Transformers
Oct 30: Lec 17Pre-training

Week 10

Nov 04: Student Lec 01RGB-D Networks
Nov 06: Student Lec 02PointNets and 3D Networks

Snapshot of Planned Schedule