
MASTERING THE MACHINE
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Computers
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26 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
A programmer builds a bot capable of playing tic-tac-toe against human opponents. The programmer enumerates every potential game board configuration and hard-codes the optimal move which the bot should take. This is an example of
A programmer builds a bot capable of playing tic-tac-toe against human opponents. The programmer enumerates every potential game board configuration and hard-codes the optimal move which the bot should take. This is an example of
A. Rule based logic
B. Graph based logic
C. Code based logic
D. Hash based logic
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
The same activation function must be used in every layer of the neural network.
The same activation function must be used in every layer of the neural network.
True
False
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
The same number of nodes must be used in each layer.
The same number of nodes must be used in each layer.
True
False
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What was the name of the first device that was based on the principle of the artificial neuron?
What was the name of the first device that was based on the principle of the artificial neuron?
Megatron
Mark II Galvatron
Iron Man Mark III armour (Jarvis Integrated)
Mark I Perceptron
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is deep learning?
What is deep learning?
A type of machine learning that uses a single layer of nodes.
A type of machine learning that uses multiple layers of nodes.
A method for data storage and retrieval.
A technique for image compression.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is a common activation function used in deep learning?
Which of the following is a common activation function used in deep learning?
Softmax
ReLU
Sigmoid
All of the above
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of an activation function in a neural network?
What is the purpose of an activation function in a neural network?
To update the weights during training
To calculate the loss function
To introduce non-linearity into the network
To initialize the weights of the network
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