CS486 - Introduction to Neural Networks and Deep Learning -Minor Test

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Across
  1. 2. No of class labels in ImageNet
  2. 4. Determines the direction of next step in CNN
  3. 6. Activation function used for Binary Class classification
  4. 8. Activation function that Squashes the input to 1 to -1
  5. 10. Unsupervised Learning rule in ANN
  6. 14. No of convolution layers in VGG16
  7. 15. Converts stimulus from the human body
  8. 16. Loss function used for classification
  9. 18. Algorithm used for Hyperparameter optimization
Down
  1. 1. Squashes the Input data to +1/2 to -1/2
  2. 3. Neural network model that uses linear activation function
  3. 5. Gate used to keep information from previous step in LSTM
  4. 6. can be done through many-to-one RNN model
  5. 7. Trainable Parameters in LSTM
  6. 9. Fundamental unit of a neural network
  7. 11. determines the depth of the convoluted image in CNN
  8. 12. Increase/ Decrease the net input of the activation function
  9. 13. GATE is a linearly inseparable data
  10. 14. Trainable Parameters in Vanilla RNN
  11. 17. No of trainable parameters in ANN