BiasesForwardpropagationStepFunctionSupervised/UnsupervisedOutputLayerConvolution(CNN)Error/CostFunctionGradientDescentInitializationRecurrent(RNN)Black Box(Interpretability)InputLayerActivationFunction/ThresholdStepFunctionRecurrent/ RNNHiddenLayerClassificationError/CostFunctionBiasesWeightsPerceptronNode/WeightedSumPerceptronBackPropagationSigmoidFunctionDeepLearningOverfittingInitializationFeedForwardClassificationWeightsActivationFunction /ThresholdOutputLayerSigmoidFunctionHiddenLayerSupervised/UnsupervisedDeepLearningConvolution/ CNNComputerVisionForwardPropagationGradientDescentInputLayerFeedForwardNode/WeightedSumComputerVisionBlackBoxBackpropagationOverfittingBiasesForwardpropagationStepFunctionSupervised/UnsupervisedOutputLayerConvolution(CNN)Error/CostFunctionGradientDescentInitializationRecurrent(RNN)Black Box(Interpretability)InputLayerActivationFunction/ThresholdStepFunctionRecurrent/ RNNHiddenLayerClassificationError/CostFunctionBiasesWeightsPerceptronNode/WeightedSumPerceptronBackPropagationSigmoidFunctionDeepLearningOverfittingInitializationFeedForwardClassificationWeightsActivationFunction /ThresholdOutputLayerSigmoidFunctionHiddenLayerSupervised/UnsupervisedDeepLearningConvolution/ CNNComputerVisionForwardPropagationGradientDescentInputLayerFeedForwardNode/WeightedSumComputerVisionBlackBoxBackpropagationOverfitting

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