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cs501r_f2016 [2016/03/31 23:06]
admin
cs501r_f2016 [2021/06/30 23:42]
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-Preliminary Syllabus and topics to be covered: 
- 
-  - Basics of DNNs 
-    - Convolution layers 
-    - Maxpooling layers 
-    - Relu units 
-    - Softmax units 
-    - Local response normalization / contrast normalization 
-  - Regularization strategies 
-    - Dropout 
-    - Dropconnect 
-    - Batch normalization 
-    - Adversarial networks 
-    - Data augmentation 
- 
-High-level implementation packages - pros and cons 
-  Tensorflow, Theano, Caffe, Keras, Torch, Mocha 
- 
-Case studies / existing networks and why they'​re interesting 
-  AlexNet 
-  VGG 
-  GoogLeNet / Inception 
-  ZFNet 
- 
-Training & initialization 
-  Initialization strategies: Xavier, Gaussian, Identity, Sparse 
-  Optimization theory and algorithms 
-  Local minima; saddle points; plateaus 
-  SGD 
-  RPROP 
-  RMS prop 
-  Adagrad 
-  Adam 
-  Higher-order algorithms (LBFGS; Hessian-free;​ trust-region) 
-  Nesterov and momentum 
- 
-Large-scale distributed learning 
-  Parameter servers 
-  Asynchronous vs. synchronous architectures 
- 
-Temporal networks and how to train them 
-  Basic RNNs 
-  LSTMs 
-  Deep Memory Nets 
- 
-Application areas 
-  Deep reinforcement learning 
-  NN models of style vs. content (deepart.io) 
-  Imagenet classification 
-  The Neural Turing Machine 
-  Sentiment classification 
-  Word embeddings 
- 
-Understanding and visualizing CNNs 
-  tSNE embeddings 
-  deconvnets 
-  data gradients / inceptionism 
- 
-Misc 
-  Network compression 
-  Low bit-precision networks 
-  Sum-product networks 
-  Evolutionary approaches to topology discovery 
-  Spatial transformer networks 
-  Network-in-network 
-  Regions-with-CNN 
  
cs501r_f2016.txt ยท Last modified: 2021/06/30 23:42 (external edit)