Event Type  Date  Description  Course Materials 

Lecture  Tuesday, Sept. 3  Intro to Deep Learning, historical context. 
[slides] [python/numpy tutorial] [jupyter tutorial] 
Lecture  Thursday, Sept. 5  Image classification and the datadriven approach knearest neighbor Linear classification 
[slides] [image classification notes] [linear classification notes] 
Lecture  Tuesday, Sept. 10  Loss functions 
[slides] 
Lecture  Thursday, Sept. 12  Optimization: Stochastic Gradient Descent and Backpropagation 
[slides] [optimization notes] 
Optional Discussion  Friday, Sept. 13  (9:0010:00am CS140) Slicing and broadcasting in Python 
[slicing and broadcasting ipynb] 
Lecture  Tuesday, Sept. 17  Backpropagation & Neural Networks I 
[slides] [backprop notes] [Efficient BackProp] (optional) related: [1], [2], [3] (optional) 
Lecture  Thursday, Sept. 19 
Neural Networks II Higherlevel representations, image features Vector, Matrix, and Tensor Derivatives 
[slides] handout 1: Vector, Matrix, and Tensor Derivatives handout 2: Derivatives, Backpropagation, and Vectorization Deep Learning [Nature] (optional) 
Optional Discussion  Friday, Sept. 20  (09:0010:00 CS140) Vector, Matrix, Tensor Derivatives and Backpropagation  [notes] 
Lecture  Tuesday, Sept. 24 
Neural Networks III Training Neural Networks I Activation Functions 
[slides] [Neural Nets notes 1] tips/tricks: [1], [2] (optional) 
Lecture  Thursday, Sept. 26 
Training Neural Networks II: weight initialization, batch normalization 
[slides] [Neural Nets notes 2] [Batch Norm] Copula Normalization (optional) 
Lecture  Tuesday, Oct. 1 
Training Neural Networks III: babysitting the learning process, hyperparameter optimization 
[slides] [Bengio 2012] (optional) 
Lecture  Thursday, Oct. 3 
Training Neural Network IV: model ensembles, dropout 
[slides] [Neural Nets notes 3] LeNet (optional) 
Optional Discussion  Friday, Oct. 4  (09:0010:00 CS142) A closer look at the maths inside batch normalization  [notes] 
Lecture  Tuesday, Oct. 8 
Training Neural Network V: parameter updates 
[slides] 
Lecture  Thursday, Oct. 10 
Convolutional Neural Networks: convolution layer, pooling layer, fully connected layer 
[slides] 
Optional Discussion  Friday, Oct. 11  (09:0010:00 CS140) Convolutional neural networks  [slides] 
No Class (Monday Schedule)  Tuesday, Oct. 15  Monday class schedule will be followed  
Lecture  Thursday, Oct. 17 
Convolutional Neural Networks: (cont.) convolution layer, pooling layer, fully connected layer 

Lecture  Tuesday, Oct. 22  ConvNets for spatial localization, Object detection 
[slides] FCN (optional) mAP (optional) 
Lecture  Thursday, Oct. 24  ConvNets for spatial localization, Object detection (cont.)  
TA Lecture  Tuesday, Oct. 29  Special topic (lecture given by Hang Su): Contentadaptive Convolutions  [slides] PACNet (optional) 
TA Lecture  Thursday, Oct. 31  Special topic (lecture given by Pia Bideau): Optical flow prediction with PWCNet  PWCNet (optional) 
Lecture  Tuesday, Nov. 5  Recurrent Neural Networks (RNN) 
[slides] DL book RNN chapter (optional) mincharrnn, charrnn, neuraltalk2 (optional) 
Lecture  Thursday, Nov. 7 
Long Short Term Memory (LSTM) 
The Unreasonable Effectiveness of RNN (optional) Understanding LSTM Networks (optional) 
Lecture  Tuesday, Nov. 12 
RNN and LSTM (cont.) 

Lecture  Thursday, Nov. 14 
Understanding and visualizing Convolutional Neural Networks Backprop into image: Visualizations, deep dream 
[slides] [visualization notes] 
Lecture  Tuesday, Nov. 19  Visualization (cont.)  
Lecture  Thursday, Nov. 21  Generative Models and GANs  [slides] 
No Class  Tuesday, Nov. 26  Thanksgiving recess  
No Class  Thursday, Nov. 28  Thanksgiving recess  
Lecture  Tuesday, Dec. 3  Training ConvNets in practice  [slides] 
Lecture  Thursday, Dec. 5  Guest Lecture by Evan Shelhamer  
Lecture  Tuesday, Dec. 10  Additional topics (TBD)  
Final  Monday, Dec. 16  Final exam (8am10am in Totman Gym) 