ForwardPropagationConvolution/ CNNHiddenLayerDeepLearningPerceptronError/CostFunctionSupervised/UnsupervisedBackPropagationInputLayerNode/WeightedSumActivationFunction/ThresholdFeedForwardOutputLayerOverfittingBlackBoxStepFunctionInitializationStepFunctionSigmoidFunctionWeightsActivationFunction /ThresholdDeepLearningConvolution(CNN)WeightsError/CostFunctionForwardpropagationComputerVisionSupervised/UnsupervisedBiasesBiasesSigmoidFunctionBlack Box(Interpretability)HiddenLayerClassificationFeedForwardBackpropagationNode/WeightedSumPerceptronInputLayerRecurrent(RNN)OutputLayerOverfittingGradientDescentInitializationComputerVisionGradientDescentClassificationRecurrent/ RNNForwardPropagationConvolution/ CNNHiddenLayerDeepLearningPerceptronError/CostFunctionSupervised/UnsupervisedBackPropagationInputLayerNode/WeightedSumActivationFunction/ThresholdFeedForwardOutputLayerOverfittingBlackBoxStepFunctionInitializationStepFunctionSigmoidFunctionWeightsActivationFunction /ThresholdDeepLearningConvolution(CNN)WeightsError/CostFunctionForwardpropagationComputerVisionSupervised/UnsupervisedBiasesBiasesSigmoidFunctionBlack Box(Interpretability)HiddenLayerClassificationFeedForwardBackpropagationNode/WeightedSumPerceptronInputLayerRecurrent(RNN)OutputLayerOverfittingGradientDescentInitializationComputerVisionGradientDescentClassificationRecurrent/ RNN

Neural Network Keyword Bingo - 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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  1. Forward Propagation
  2. Convolution / CNN
  3. Hidden Layer
  4. Deep Learning
  5. Perceptron
  6. Error/Cost Function
  7. Supervised/ Unsupervised
  8. Back Propagation
  9. Input Layer
  10. Node/ Weighted Sum
  11. Activation Function/ Threshold
  12. Feed Forward
  13. Output Layer
  14. Overfitting
  15. Black Box
  16. Step Function
  17. Initialization
  18. Step Function
  19. Sigmoid Function
  20. Weights
  21. Activation Function / Threshold
  22. Deep Learning
  23. Convolution (CNN)
  24. Weights
  25. Error/Cost Function
  26. Forward propagation
  27. Computer Vision
  28. Supervised/Unsupervised
  29. Biases
  30. Biases
  31. Sigmoid Function
  32. Black Box (Interpretability)
  33. Hidden Layer
  34. Classification
  35. Feed Forward
  36. Back propagation
  37. Node/Weighted Sum
  38. Perceptron
  39. Input Layer
  40. Recurrent (RNN)
  41. Output Layer
  42. Overfitting
  43. Gradient Descent
  44. Initialization
  45. Computer Vision
  46. Gradient Descent
  47. Classification
  48. Recurrent / RNN