Data Science and Machine Learning (Theory and Projects) A to Z - RNN Implementation: Automatic Differentiation

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Information Technology (IT), Architecture
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Hard
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5 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is one of the main challenges in computing gradients for recurrent neural networks?
Insufficient data for training
Overfitting the model
Difficulty in keeping track of derivatives and memory information
Lack of computational power
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does automatic differentiation simplify the process of gradient computation?
By increasing the speed of computation
By automatically computing gradients without manual tracking
By eliminating the need for a forward pass
By reducing the size of the neural network
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a noted disadvantage of automatic differentiation compared to analytical solutions?
It is slightly slower
It is less accurate
It is more complex to implement
It requires more memory
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is a package that offers automatic differentiation?
PyTorch
Scikit-learn
NumPy
OpenCV
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which package will be used for implementing projects at the end of the module?
TensorFlow
PyTorch
Caffe
MaxNet
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