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question

We are training fully connected network with two hidden layers to predict housing prices. Inputs are 100100-dimensional, and have several features such as the number of square feet, the median family income, etc. The first hidden layer has 10001000 activations. The second hidden layer has 1010 activations. The output is a scalar representing the house price. Assuming a vanilla network with affine transformations and with no batch normalization and no learnable parameters in the activation function, how many parameters does this network have?
Plain-text mathematical notation (without MathML)
We are training fully connected network with two hidden layers to predict housing prices. Inputs are 100-dimensional, and have several features such as the number of square feet, the median family income, etc. The first hidden layer has 1000 activations. The second hidden layer has 10 activations. The output is a scalar representing the house price. Assuming a vanilla network with affine transformations and with no batch normalization and no learnable parameters in the activation function, how many parameters does this network have?
Original LaTeX notation
We are training fully connected network with two hidden layers to predict housing prices. Inputs are $100$-dimensional, and have several features such as the number of square feet, the median family income, etc. The first hidden layer has $1000$ activations. The second hidden layer has $10$ activations. The output is a scalar representing the house price. Assuming a vanilla network with affine transformations and with no batch normalization and no learnable parameters in the activation function, how many parameters does this network have?

choices

  1. 111021
  2. 110010
  3. 111110
  4. 110011

subject

machine_learning

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Source and history

task_id
1467de58-5a9d-5d36-8e71-647bd82639bb
task_key
all--test--1467de58-5a9d-5d36-8e71-647bd82639bb
task_revision_id
1
config / split
all / test

Official source

initial import