filteroutputchannelsupervisedcnnkernelsize ofconvolutionalfilterlabelsupervisedsigmoidactivationfunction3Dinputimageenvironmentreinforcementlstmsequencepredictiondropoutnullifysomeneurondeepnetworkmultiplelayersstridenumberof pixelsshiftsdensefullyconnectedflatten1DbrainbiologicalneuralnetworkpaddingouterlayerrelunegativevaluesCconvolutionfeatureextractionpoolingsizereducefeaturemapconvolutionallayernodeneuronactivationfunctionneuronactivateunsupervisedclusteringunlabeledcnnneworkarchitecturefilteroutputchannelsupervisedcnnkernelsize ofconvolutionalfilterlabelsupervisedsigmoidactivationfunction3Dinputimageenvironmentreinforcementlstmsequencepredictiondropoutnullifysomeneurondeepnetworkmultiplelayersstridenumberof pixelsshiftsdensefullyconnectedflatten1DbrainbiologicalneuralnetworkpaddingouterlayerrelunegativevaluesCconvolutionfeatureextractionpoolingsizereducefeaturemapconvolutionallayernodeneuronactivationfunctionneuronactivateunsupervisedclusteringunlabeledcnnneworkarchitecture

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.


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