Machine Learning Algorithms – Part II Exercises (Part 2)
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Question 1 of 8
1. Question
There is only one type of deep learning neural network.
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Question 2 of 8
2. Question
What are some problems with deep learning you need to keep in mind? (Select all that apply):
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Question 3 of 8
3. Question
Applications of Generative Adversarial Networks (GANs) include (select all that apply):
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Question 4 of 8
4. Question
Match the Deep Learning term to the definition:
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- Deep Learning
- Feed-Forward Neural Network
- Backpropagation
- Gradient Descent
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A machine learning method that uses neural networks with multiple hidden layers
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A neural network in which inputs flow from one side of the network forward to one or more hidden layers and finally to the output
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A method used to accelerate the training of neural networks by calculating gradients for the weights used in a specific trained neural network
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A method used to facilitate training of deep learning neural network machine learning models by optimizing and finding the best network parameters (including bias and weights) to minimize the model’s cost function and thereby get to the desired results more quickly
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Question 5 of 8
5. Question
Match the machine learning training term to the definition:
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- Epoch
- Batch
- Learning Curve
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An iteration of training a model on the training data set.
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Training done in groups with the gradients then calculated and aggregated across those batches.
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A quantified measurement over time of how well a particular machine learning model is learning from the data it has been given
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Question 6 of 8
6. Question
What is a Boltzmann Machine?
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Question 7 of 8
7. Question
Which applications are most commonly used with Recurrent Neural Networks (RNN)?
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Question 8 of 8
8. Question
Convolutional Neural Networks (CNNs) have been applied to a number of applications. Select all that apply:
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