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Deep Learning MCQ's

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Which of the following is a subset of machine learning?
S Deep Learning
A
B
C
Explanation
Deep learning is a computer software that mimics the network of neurons in a brain. It is a subset of machine learning and is called deep learning.
D
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How many layers Deep learning algorithms are constructed?
S Deep Learning
A
B
Explanation
Deep learning algorithms are constructed with 3 connected layers : inner layer, outer layer, hidden layer.
C
D
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The first layer is called the?
S Deep Learning
A
Explanation
The first layer is called the Input Layer. The last layer is called the Output Layer. All layers in between are called Hidden Layers.
B
C
D
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RNNs stands for?
S Deep Learning
A
B
C
D
Explanation
Recurrent neural networks (RNNs) : RNN is a multi-layered neural network that can store information in context nodes, allowing it to learn data sequences and output a number or another sequence.
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Which of the following is/are Common uses of RNNs?
S Deep Learning
A
B
C
D
Explanation
All of the above are Common uses of RNNs.
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Which of the following is well suited for perceptual tasks?
S Deep Learning
A
B
C
Explanation
CNN is a multi-layered neural network with a unique architecture designed to extract increasingly complex features of the data at each layer to determine the output. CNNs are well suited for perceptual tasks.
D
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CNN is mostly used when there is an?
S Deep Learning
A
B
Explanation
CNN is mostly used when there is an unstructured data set (e.g., images) and the practitioners need to extract information from it.
C
D
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Which neural network has only one hidden layer between the input and output?
S Deep Learning
A
Explanation
Shallow neural network: The Shallow neural network has only one hidden layer between the input and output.
B
C
D
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Which of the following is/are Limitations of deep learning?
S Deep Learning
A
B
C
Explanation
Both A and B are Limitations of deep learning.
D
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Deep learning algorithms are _______ more accurate than machine learning algorithm in image classification.
S Deep Learning
A
B
C
D
Explanation
Deep learning can outperform traditional method. For instance, deep learning algorithms are 41% more accurate than machine learning algorithm in image classification, 27 % more accurate in facial recognition and 25% in voice recognition.
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