Best 50+ Neural Network MCQ With Revision Notes

Neural Network MCQ: Narrow Artificial Intelligence is used to train specific tasks without cognitive abilities and understanding like humans. Some of the areas where Neural AI is used such as voice recognition like Siri, Google Assistant and Alexa, Neural AI can also analyze the image like self-driving cars etc.

Neural Network MCQ With Revision Notes

Real World Narrow AI Application

1. Recommendation System – There are a lot of different types of applications based on Narrow AI like Amazon, Spotify and Netflix which can analyze user behavior and recommend products, songs or movies.
2. Voice assistants – There are different types of applications based on voice command and perform tasks like phone calls and answering questions, for example Google Assistant, Siri and Alexa.
3. Weather Forecasting – Narrow AI can predict temperature and different weather conditions based on the climate data.
4. Email filtering – Some of the Email providers also used Narrow AI services for example Gmail, Gmail use Narrow Ai to find spam emails.

Benefits of Narrow AI?

1. Narrow AI can perform tasks more efficiently than humans.
2. Narrow AI can work 24/7.
3. Narrow AI can reduce human error.
4. Dangerous or repetitive tasks can be easily handled by Narrow AI
5. Narrow AI can detect the image.
6. Narrow AI can understand the generated human language.
7. Based on the user preferences and behavior, Narrow AI can perform tasks.
8. Narrow AI can make decisions in Autonomous Vehicles.
9. Narrow AI can diagnose diseases from medical images.
10. Narrow AI can handle customer inquiries and support.

Neurons

• Neural networks have a artificial neurons,
• It is also known as perceptrons or nodes.
• The basic purpose of NN is to take inputs, perform calculations, and produce outputs.

Layers

• There are three different types of Neural networks layers
  • Input Layer
  • Hidden Layers
  • Output Layer

Weights and Biases

• Neurons have an associated weight, which determines the strength of the connection.
• Neurons also have a bias which helps to adjust the output.

Activation Functions

• Activation functions enable a complex function and it is nonlinearity into the network.
• Activation functions include sigmoid, tanh, and ReLU (Rectified Linear Unit).

Feedforward and Backpropagation

• Neural networks can flow in one direction.
• Neural networks can be feedforward (data flows in one direction) or recurrent (with loops).
• Backpropagation algorithm is used for training feedforward networks.

Training Data

• Neural networks used labeled training data to learn.
• This data is divided into training, validation, and testing sets to evaluate and improve the model performance.

Loss Function

• A loss function measures the difference between predicted and actual outputs.
• The goal of training is to minimize this loss and improve the performance.

Overfitting and Regularization

• Neural networks can overfit the training data if there is any complex data.
• Regularization techniques for example dropout and weight decay helps to prevent overfitting.

Deep Learning

• Deep neural networks are also known as deep learning models.
• Deep neural networks have multiple hidden layers.
• They are particularly effective at learning hierarchical features from data and have achieved remarkable success in various applications, including computer vision and natural language processing.

Convolutional Neural Networks (CNNs)

• CNNs are a type of neural network.
• CNNs are designed for a special purpose like data processing, images processing and videos processing.
• CNNs used convolutional layers to automatically learn and extract features from visual data.

Recurrent Neural Networks (RNNs)

• RNNs are designed for sequential data such as time series or natural language.
• They use loops to maintain and update hidden states across time steps.

Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)

• These are specialized RNN architectures which can vanish gradient problems and used for capturing long-range dependencies in sequential data.

Transfer Learning

• Transfer learning uses pre-trained neural network models as a starting point and fine-tuning for specific tasks.

Hardware Acceleration

• Training deep neural networks can be computationally intensive.
• GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) are commonly used for accelerating neural network training.

Ethical Considerations

• As neural networks are increasingly used in real-world applications, ethical considerations related to bias, fairness, transparency, and privacy are crucial.

Interpretability

• Understanding and interpreting the decisions made by neural networks, especially deep learning models, is an ongoing research challenge.

Neural Network MCQ with answers

1. What does Narrow AI mean ____________.
   a. AI that specializes in a specific task
   b. AI with human intelligence
   c. AI capable to understand emotion
   d. AI that understands the human life

2. Which of the following is an example of Narrow AI?
   a. Self driving cars
   b. Autonomous robots
   c. Human managed chatbots
   d. AI with emotions

3. What is the main characteristic of Narrow AI systems?
   a. They can perform intellectual task like human
   b. They possess consciousness and self awareness
   c. They excel in a single specific task
   d. They can understand the emotions

4. _______________ type of AI focused on replicating human cognitive abilities?
   a. Narrow AI
   b. General AI
   c. Superintelligent AI
   d. None of the above

5. Which of the following does not belong to Narrow AI?
   a. Ability to learn from data
   b. Adaptation to various tasks
   c. Human like reasoning and understanding
   d. None of the above

6. _______________ AI is used to design Siri and Google Assistant?
   a. Narrow AI
   b. Artificial General Intelligence
   c. Strong AI
   d. Superintelligent AI

7. _____________ AI has the capability to perform like humans.
   a. Narrow AI
   b. Weak AI
   c. Artificial Superintelligence
   d. Humanoid AI

8. Which AI is also known as Weak AI?
   a. Narrow AI
   b. Strong AI
   c. Superintelligent AI
   d. General AI

9. ___________ is an application of Narrow AI?
   a. Spam email filtering
   b. Music recommendation services
   c. Autonomous vehicles
   d. All of the above

10. What function does the bias term provide in a neuron?
    a. It represents the weight of the neuron
    b. It introduces randomness to the neuron
    c. It shifts the activation function
    d. None of the above

11. What is the main function of a neural network’s output layer?
    a. To perform feature extraction
    b. To make predictions or classifications
    c. To introduce non-linearity
    d. None of the above

12. Which particular neural network architecture aims to resemble the human brain?
    a. Convolutional Neural Network (CNN)
    b. Recurrent Neural Network (RNN)
    c. Multilayer Perceptron (MLP)
    d. None of the above

13. What do you mean by backpropagation ______________ in neural networks?
    a. Forward pass of data
    b. Training process for adjusting weights
    c. Activation of output layer
    d. None of the above

14. In a feedforward neural network, information flows ___________.
    a. Only in the forward direction
    b. Only in the backward direction
    c. In both forward and backward directions
    d. In random directions

15. _______________ layer is responsible for input processing in the neural network.
    a. Input layer
    b. Output layer
    c. Hidden layer
    d. Activation layer

16. What does neural network weight initialization provide?
    a. To give weights random values
    b. Initializing all weights to zero.
    c. To set weights up initially based on particular standards
    d. To eliminate network weights

17. Which neural network layout is best for tasks involving image classification?
    a. Recurrent Neural Network (RNN)
    b. Multilayer Perceptron (MLP)
    c. Convolutional Neural Network (CNN)
    d. Radial Basis Function Network (RBFN)

18. Which training method tries to reduce the discrepancy between expected and actual results?
    a. Gradient Descent
    b. K-means clustering
    c. Principal Component Analysis (PCa.
    d. None of the above

19. What is the basic building block of a neural network?
    a. Neuron
    b. Node
    c. Weight
    d. Layer

20. Which of the following functions is commonly used to activate neural networks?
    a. Linear function
    b. Sigmoid function
    c. Square root function
    d. Exponential function

21. What type of neural network is usually used to handle sequential data, such as natural language?
    a. Recurrent Neural Network (RNN)
    b. Multilayer Perceptron (MLP)
    c. Convolutional Neural Network (CNN)
    d. Radial Basis Function Network (RBFN)

22. What is batch normalization’s main benefit when employed in neural networks?
    a. Faster training
    b. Smaller model size
    c. Improved weight initialization
    d. None of the above

23. What purpose does the loss play in a neural network?
    a. To initialize the weights
    b. To define the architecture of the network
    c. To measure the error between predicted and actual outputs
    d. None of the above

24. What specific neural network architecture is designed for unsupervised learning tasks?
    a. Convolutional Neural Network (CNN)
    b. Recurrent Neural Network (RNN)
    c. Autoencoder
    d. None of the above

25. What is overfitting in the neural networks?
    a. Model that performs well on training data but poorly on new data
    b. A model that works equally well on both training and testing data
    c. A model with too few layers
    d. None of the above

26. What kind of layer is typically used to a neural network to add non-linearity?
    a. Input layer
    b. Hidden layer
    c. Output layer
    d. None of the above

27. What are the main applications of convolutional layers in a convolutional neural network (CNN)?
    a. Feature extraction
    b. Classification
    c. Storing weights
    d. None of the above

28. What purpose is served by the dropout layer in a neural network?
    a. To increase the number of neurons in a layer
    b. To remove random neurons during training to prevent overfitting
    c. To normalize the input data
    d. None of the above

29. A neural network with numerous hidden layers is known as __.
    a. Shallow network
    b. Deep network
    c. Wide network
    d. Linear network

30. What does the gradient descent optimization learning rate do?
    a. To determine the size of weight updates
    b. To set the initial weights
    c. To define the number of hidden layers
    d. None of the above

31. What does the vanishing gradient problem in deep neural networks mean?
    a. It refers to the gradient descent algorithm failing to converge.
    b. It occurs when gradients become too large, causing instability.
    c. It occurs when gradients become too small, hindering training.
    d. None of the above

32. What does the output layer of a neural network’s softmax activation function do?
    a. To introduce non-linearity
    b. To calculate the mean of outputs
    c. To convert raw scores into probability distributions
    d. None of the above

33. Which neural network design is best for forecasting time-series data?
    a. Recurrent Neural Network (RNN)
    b. Convolutional Neural Network (CNN)
    c. Multilayer Perceptron (MLP)
    d. Radial Basis Function Network (RBFN)

34. What role does the kernel play in the convolutional layer of a CNN?
    a. To compute the gradient
    b. To initialize the weights
    c. To perform convolution operations on input data
    d. None of the above

35. Which optimization method is reputed to be versatile in terms of learning rates for different parameters?
    a. Stochastic Gradient Descent (SGd.
    b. Adam
    c. Gradient Descent
    d. None of the above

36. What does “transfer learning” mean in terms of neural networks?
    a. Training a network to perform transfers between accounts
    b. Using a pre-trained model as a starting point for a new task
    c. Transferring weights between layers in a network
    d. None of the above

37. What does the term “momentum” signify when referring to optimization methods like SGD?
    a. To add random noise to weight updates
    b. To speed up convergence by considering previous weight updates
    c. To initialize the weights of the network
    d. None of the above

38. What are the main advantages of a recurrent neural network (RNN) using LSTM (Long Short-Term Memory) cells?
    a. Faster training
    b. Improved memory of past information
    c. Smaller model size
    d. None of the above

39. What kind of neural network performs generative tasks like producing visuals the best?
    a. Recurrent Neural Network (RNN)
    b. Convolutional Neural Network (CNN)
    c. Variational Autoencoder (VAE)
    d. Radial Basis Function Network (RBFN)

40. What is the primary disadvantage of the fully connected layers in neural networks?
    a. They require fewer weights.
    b. They can’t handle sequential data.
    c. They have too many connections, leading to overfitting.
    d. None of the above

41. What use does the recurrent layer of a neural network serve?
    a. To introduce non-linearity
    b. To model sequential data and dependencies
    c. To normalize input data
    d. None of the above

42. What is the biggest challenge in deep learning?
    a. Lack of available data
    b. Lack of computing power
    c. Vanishing gradient problem
    d. None of the above

43. What type of neural network is typically employed for tasks involving natural language processing, such as language translation?
    a. Recurrent Neural Network (RNN)
    b. Convolutional Neural Network (CNN)
    c. Multilayer Perceptron (MLP)
    d. Radial Basis Function Network (RBFN)

44. What are dropout’s main benefits in neural networks?
    a. It reduces the number of layers in the network.
    b. It prevents overfitting by randomly disabling neurons during training.
    c. It increases the size of the training dataset.
    d. None of the above

45. What does a hyperparameter for a neural network actually mean?
    a. A weight assigned to neurons
    b. A parameter learned during training
    c. A setting that is not learned but affects the model’s behavior
    d. None of the above

46. What purpose does the validation dataset play in a neural network’s training process?
    a. It is used to train the model.
    b. It is used to monitor the model’s performance on unseen data.
    c. It is used to test the model’s generalization.
    d. None of the above

47. What purpose does a neural network’s learning rate scheduler serve?
    a. To adjust the learning rate during training
    b. To set the initial weights of the network
    c. To normalize input data
    d. None of the above

48. What does “epoch” convey in terms of neural network training?
    a. A single forward pass of the data
    b. A complete iteration through the training dataset
    c. A single weight update
    d. None of the above

49. What drawback does using a high learning rate have on gradient descent optimization?
    a. Slow convergence
    b. Overshooting the minimum of the loss function
    c. Increased risk of local minima
    d. None of the above

50. In sequence-to-sequence applications like machine translation, what kind of neural network layer is commonly used?
    a. Convolutional layer
    b. Pooling layer
    c. Encoder-Decoder layer
    d. None of the above

51. Which activation function is typically used in the output layer of a regression neural network?
    a. Sigmoid
    b. ReLU
    c. Linear
    d. None of the above

52. What advantage does neural network mini-batch training have over other techniques?
    a. Faster convergence
    b. Lower memory requirements
    c. Simpler implementation
    d. None of the above

53. Which neural network design is most effective for identifying distant dependencies in sequential data?
    a. Recurrent Neural Network (RNN)
    b. Convolutional Neural Network (CNN)
    c. Transformer
    d. None of the above

54. What kind of neural network layer downsamples the input data for a CNN?
    a. Convolutional layer
    b. Pooling layer
    c. Recurrent layer
    d. None of the above

Chapterwise MCQs on Artificial Intelligence

• Artificial Intelligence MCQ With Revision Notes
• Neural Network MCQ With Revision Notes
• Expert System MCQ With Revision Notes
• Deep Learning MCQ With Revision Notes
• Data Science MCQ With Revision Notes
• Computer Vision MCQ With Revision Notes
• Machine Learning MCQ With Revision Notes

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