StepFunctionActivationFunction /ThresholdClassificationWeightsHiddenLayerRecurrent(RNN)OutputLayerConvolution(CNN)BiasesHiddenLayerActivationFunction/ThresholdBackPropagationComputerVisionRecurrent/ RNNStepFunctionOverfittingFeedForwardClassificationBlack Box(Interpretability)GradientDescentInputLayerInputLayerFeedForwardDeepLearningError/CostFunctionConvolution/ CNNSupervised/UnsupervisedSigmoidFunctionPerceptronNode/WeightedSumInitializationPerceptronBlackBoxForwardpropagationError/CostFunctionWeightsDeepLearningForwardPropagationNode/WeightedSumGradientDescentOverfittingSupervised/UnsupervisedComputerVisionSigmoidFunctionOutputLayerInitializationBiasesBackpropagationStepFunctionActivationFunction /ThresholdClassificationWeightsHiddenLayerRecurrent(RNN)OutputLayerConvolution(CNN)BiasesHiddenLayerActivationFunction/ThresholdBackPropagationComputerVisionRecurrent/ RNNStepFunctionOverfittingFeedForwardClassificationBlack Box(Interpretability)GradientDescentInputLayerInputLayerFeedForwardDeepLearningError/CostFunctionConvolution/ CNNSupervised/UnsupervisedSigmoidFunctionPerceptronNode/WeightedSumInitializationPerceptronBlackBoxForwardpropagationError/CostFunctionWeightsDeepLearningForwardPropagationNode/WeightedSumGradientDescentOverfittingSupervised/UnsupervisedComputerVisionSigmoidFunctionOutputLayerInitializationBiasesBackpropagation

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.


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