stridenumberof pixelsshiftspoolingsizereducesupervisedcnnsigmoidactivationfunctionpaddingouterlayerdropoutnullifysomeneuronfeaturemapconvolutionallayercnnneworkarchitecturesupervisedregressionenvironmentreinforcementrelunegativevalueslstmsequencepredictionflatten1Dunsupervisedclusteringactivationfunctionneuronactivate3DinputimageunlabeledCconvolutionfeatureextractiondeepnetworkmultiplelayersdensefullyconnectedlabelsupervisedfilteroutputchannelnodeneuronkernelsize ofconvolutionalfilterstridenumberof pixelsshiftspoolingsizereducesupervisedcnnsigmoidactivationfunctionpaddingouterlayerdropoutnullifysomeneuronfeaturemapconvolutionallayercnnneworkarchitecturesupervisedregressionenvironmentreinforcementrelunegativevalueslstmsequencepredictionflatten1Dunsupervisedclusteringactivationfunctionneuronactivate3DinputimageunlabeledCconvolutionfeatureextractiondeepnetworkmultiplelayersdensefullyconnectedlabelsupervisedfilteroutputchannelnodeneuronkernelsize ofconvolutionalfilter

CNN - Call List

(Print) Use this randomly generated list as your call list when playing the game. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


1
B B
2
I I
3
G G
4
B B
5
N N
6
I I
7
O O
8
I I
9
O O
10
N N
11
G G
12
O O
13
O O
14
G G
15
G G
16
B B
17
I
18
B B
19
N N
20
I I
21
B B
22
G G
23
O O
24
N N
  1. B-number of pixels shifts
    B-stride
  2. I-size reduce
    I-pooling
  3. G-cnn
    G-supervised
  4. B-activation function
    B-sigmoid
  5. N-outer layer
    N-padding
  6. I-nullify some neuron
    I-dropout
  7. O-convolutional layer
    O-feature map
  8. I-nework architecture
    I-cnn
  9. O-regression
    O-supervised
  10. N-reinforcement
    N-environment
  11. G-negative values
    G-relu
  12. O-sequence prediction
    O-lstm
  13. O-1D
    O-flatten
  14. G-clustering
    G-unsupervised
  15. G-neuron activate
    G-activation function
  16. B-input image
    B-3D
  17. I-unlabeled
  18. B-feature extraction
    B-Cconvolution
  19. N-multiple layers
    N-deep network
  20. I-fully connected
    I-dense
  21. B-supervised
    B-label
  22. G-output channel
    G-filter
  23. O-neuron
    O-node
  24. N-size of convolutional filter
    N-kernel