dropoutnullifysomeneuronlabelsupervisedunlabeledkernelsize ofconvolutionalfilterdeepnetworkmultiplelayersrelunegativevalueslstmsequencepredictionactivationfunctionneuronactivatenodeneuroncnnneworkarchitectureenvironmentreinforcementsigmoidactivationfunctionfilteroutputchannelbrainbiologicalneuralnetwork3Dinputimagepoolingsizereducestridenumberof pixelsshiftspaddingouterlayerfeaturemapconvolutionallayerdensefullyconnectedsupervisedcnnflatten1DunsupervisedclusteringCconvolutionfeatureextractiondropoutnullifysomeneuronlabelsupervisedunlabeledkernelsize ofconvolutionalfilterdeepnetworkmultiplelayersrelunegativevalueslstmsequencepredictionactivationfunctionneuronactivatenodeneuroncnnneworkarchitectureenvironmentreinforcementsigmoidactivationfunctionfilteroutputchannelbrainbiologicalneuralnetwork3Dinputimagepoolingsizereducestridenumberof pixelsshiftspaddingouterlayerfeaturemapconvolutionallayerdensefullyconnectedsupervisedcnnflatten1DunsupervisedclusteringCconvolutionfeatureextraction

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