3Dinputimagestridenumberof pixelsshiftsflatten1Ddensefullyconnectedfeaturemapconvolutionallayerunsupervisedclusteringpoolingsizereducesupervisedcnnactivationfunctionneuronactivatesupervisedregressionlstmsequencepredictiondeepnetworkmultiplelayersnodeneuronsigmoidactivationfunctionunlabeledkernelsize ofconvolutionalfilterdropoutnullifysomeneuroncnnneworkarchitecturelabelsupervisedCconvolutionfeatureextractionenvironmentreinforcementfilteroutputchannelpaddingouterlayerrelunegativevalues3Dinputimagestridenumberof pixelsshiftsflatten1Ddensefullyconnectedfeaturemapconvolutionallayerunsupervisedclusteringpoolingsizereducesupervisedcnnactivationfunctionneuronactivatesupervisedregressionlstmsequencepredictiondeepnetworkmultiplelayersnodeneuronsigmoidactivationfunctionunlabeledkernelsize ofconvolutionalfilterdropoutnullifysomeneuroncnnneworkarchitecturelabelsupervisedCconvolutionfeatureextractionenvironmentreinforcementfilteroutputchannelpaddingouterlayerrelunegativevalues

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