
Exploring Deep Learning Concepts

Quiz
•
Engineering
•
12th Grade
•
Hard
Ceronmani V
FREE Resource
15 questions
Show all answers
1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is deep learning?
Deep learning is a method of data storage that uses cloud technology.
Deep learning is a form of data visualization that represents information graphically.
Deep learning is a subset of machine learning that uses neural networks with many layers to model complex patterns in data.
Deep learning is a type of traditional programming that relies on explicit algorithms.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does deep learning differ from traditional machine learning?
Deep learning differs from traditional machine learning in that it uses multi-layered neural networks to automatically learn features from data, while traditional methods rely on manual feature extraction and simpler algorithms.
Deep learning is only applicable to structured data, while traditional methods handle unstructured data better.
Deep learning requires more manual feature extraction than traditional machine learning.
Traditional machine learning uses deep neural networks for feature learning.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are neural networks?
Neural networks are a type of machine learning model that simulate the way human brains operate to recognize patterns and make decisions.
Neural networks are algorithms that only work with structured data.
Neural networks are a form of traditional programming that follows strict rules.
Neural networks are a type of hardware used for data storage.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the role of activation functions in neural networks?
Activation functions enable neural networks to learn non-linear relationships.
Activation functions are responsible for data preprocessing before training.
Activation functions are used to initialize weights in neural networks.
Activation functions help in reducing the size of the neural network.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Explain the concept of overfitting in deep learning.
Overfitting occurs when a model is too simple and cannot capture the underlying patterns in the data.
Overfitting is when a model performs equally well on both training and unseen data.
Overfitting in deep learning is when a model performs well on training data but poorly on unseen data due to excessive learning of noise and details.
Overfitting happens when a model is trained on too little data, leading to poor generalization.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a convolutional neural network (CNN)?
A convolutional neural network (CNN) is a shallow learning model that processes unstructured data.
A convolutional neural network (CNN) is a type of recurrent neural network used for time series analysis.
A convolutional neural network (CNN) is a deep learning model designed for processing grid-like data, primarily used in image analysis.
A convolutional neural network (CNN) is primarily used for natural language processing tasks.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How do recurrent neural networks (RNNs) work?
RNNs are designed to forget all previous inputs after processing each one.
RNNs work by maintaining a hidden state that updates with each input in a sequence, allowing them to learn from previous inputs.
RNNs process inputs in parallel without any hidden state.
RNNs only work with fixed-size input data and do not handle sequences.
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