1. What is a neural network?
A) A type of decision tree
B) A framework for modeling complex relationships between inputs and outputs
C) A linear regression model
D) A clustering algorithm
Answer: B) A framework for modeling complex relationships between inputs and outputs
2. Which of the following is the most basic unit in a neural network?
A) Neuron
B) Layer
C) Epoch
D) Bias
Answer: A) Neuron
3. In a neural network, what does the term “epoch” refer to?
A) The number of hidden layers
B) The number of times the entire dataset is passed forward and backward through the neural network
C) The activation function used
D) The number of neurons in the input layer
Answer: B) The number of times the entire dataset is passed forward and backward through the neural network
4. Which of the following activation functions is commonly used in the output layer for binary classification tasks?
A) ReLU
B) Sigmoid
C) Tanh
D) Softmax
Answer: B) Sigmoid
5. What is the purpose of the activation function in a neural network?
A) To introduce non-linearity into the model
B) To initialize weights
C) To reduce overfitting
D) To combine input features
Answer: A) To introduce non-linearity into the model
6. Which of the following is a common problem when training deep neural networks?
A) Vanishing gradients
B) Feature scaling
C) Underfitting
D) Redundant weights
Answer: A) Vanishing gradients
7. Which technique is often used to prevent overfitting in neural networks?
A) Regularization
B) Data augmentation
C) Dropout
D) All of the above
Answer: D) All of the above
8. Which of the following is a typical characteristic of a deep neural network?
A) Multiple hidden layers
B) One output layer only
C) Use of k-means clustering
D) Linear relationships only
Answer: A) Multiple hidden layers
9. What is “backpropagation” in neural networks?
A) A process of adjusting weights in a neural network based on the error rate
B) A method of combining input features
C) A way to initialize weights
D) A data preprocessing technique
Answer: A) A process of adjusting weights in a neural network based on the error rate
10. What is a “convolutional neural network” (CNN) primarily used for?
A) Image and video recognition
B) Time series forecasting
C) Text classification
D) Clustering
Answer: A) Image and video recognition
11. Which of the following is a commonly used optimization algorithm in training neural networks?
A) Stochastic Gradient Descent (SGD)
B) k-Nearest Neighbors (k-NN)
C) Decision Tree
D) Naive Bayes
Answer: A) Stochastic Gradient Descent (SGD)
12. What is the purpose of the “softmax” function in the output layer of a neural network?
A) To convert logits into probabilities
B) To initialize weights
C) To reduce dimensionality
D) To standardize input features
Answer: A) To convert logits into probabilities
13. Which of the following is true about the “ReLU” activation function?
A) It outputs zero for any negative input value
B) It is used exclusively in the output layer
C) It is only applicable in regression tasks
D) It always outputs a value between 0 and 1
Answer: A) It outputs zero for any negative input value
14. In a neural network, what does the term “bias” refer to?
A) An extra parameter in the neuron used to adjust the output along with the weighted sum of inputs
B) The error term in the prediction
C) The learning rate of the network
D) The difference between the predicted and actual output
Answer: A) An extra parameter in the neuron used to adjust the output along with the weighted sum of inputs
15. What is “dropout” in the context of neural networks?
A) A regularization technique to prevent overfitting by randomly setting a fraction of neurons to zero during training
B) A method for initializing weights
C) A type of activation function
D) A preprocessing step for input data
Answer: A) A regularization technique to prevent overfitting by randomly setting a fraction of neurons to zero during training
16. Which of the following is a challenge in training very deep neural networks?
A) Vanishing or exploding gradients
B) Overfitting with too little data
C) Selecting an appropriate learning rate
D) All of the above
Answer: D) All of the above
17. Which type of neural network is particularly well-suited for processing sequential data, such as time series?
A) Recurrent Neural Network (RNN)
B) Convolutional Neural Network (CNN)
C) Feedforward Neural Network (FNN)
D) Autoencoder
Answer: A) Recurrent Neural Network (RNN)
18. What is “weight initialization” in neural networks?
A) The process of setting the initial values of weights before training begins
B) The final step in training a neural network
C) The process of tuning hyperparameters
D) The process of applying dropout
Answer: A) The process of setting the initial values of weights before training begins
19. Which of the following describes a “fully connected” layer in a neural network?
A) Every neuron in the layer is connected to every neuron in the previous and next layer
B) The layer contains only one neuron
C) The layer contains neurons with no activation functions
D) The layer is only connected to the input layer
Answer: A) Every neuron in the layer is connected to every neuron in the previous and next layer
20. What does “LSTM” stand for in the context of neural networks?
A) Long Short-Term Memory
B) Linear Sequential Training Model
C) Least Squares Temporal Mapping
D) Logistic Simple Training Method
Answer: A) Long Short-Term Memory
21. What is the purpose of a “learning rate” in training a neural network?
A) To control the size of the steps the model takes during gradient descent
B) To set the number of neurons in each layer
C) To initialize the weights of the network
D) To determine the size of the training data
Answer: A) To control the size of the steps the model takes during gradient descent
22. What is “gradient descent” in the context of neural networks?
A) An optimization algorithm used to minimize the loss function
B) A type of activation function
C) A regularization technique
D) A method for data normalization
Answer: A) An optimization algorithm used to minimize the loss function
23. Which of the following is NOT a type of neural network?
A) Convolutional Neural Network (CNN)
B) Recurrent Neural Network (RNN)
C) Support Vector Machine (SVM)
D) Feedforward Neural Network (FNN)
Answer: C) Support Vector Machine (SVM)
24. Which of the following is true about “batch normalization”?
A) It normalizes the input features of each mini-batch
B) It is used to prevent overfitting by randomly setting activations to zero
C) It is an activation function
D) It is only used in the output layer
Answer: A) It normalizes the input features of each mini-batch
25. In a neural network, what does the term “overfitting” refer to?
A) The model performs well on training data but poorly on unseen data
B) The model performs poorly on both training and test data
C) The model has too few parameters to capture the underlying patterns
D) The model’s weights are not properly initialized
Answer: A) The model performs well on training data but poorly on unseen data
26. What is the purpose of using “dropout” during training?
A) To prevent overfitting by randomly dropping units during training
B) To improve the accuracy of the model
C) To speed up the training process
D) To enhance the regularization of the model
Answer: A) To prevent overfitting by randomly dropping units during training
27. Which neural network architecture is best suited for natural language processing tasks?
A) Recurrent Neural Network (RNN)
B) Convolutional Neural Network (CNN)
C) Feedforward Neural Network (FNN)
D) Radial Basis Function Network (RBFN)
Answer: A) Recurrent Neural Network (RNN)
28. Which of the following is NOT a commonly used activation function?
A) Tanh
B) Softmax
C) Linear
D) AdaBoost
Answer: D) AdaBoost
29. What is the role of “Adam” optimizer in neural networks?
A) It is an optimization algorithm used to adjust weights and biases
B) It is a type of activation function
C) It is a method for weight initialization
D) It is used for data preprocessing
Answer: A) It is an optimization algorithm used to adjust weights and biases
30. Which of the following describes “autoencoders”?
A) Neural networks used for unsupervised learning to learn efficient representations of data
B) Networks used to process sequential data
C) Networks used exclusively for image classification
D) Networks used to detect anomalies in data
Answer: A) Neural networks used for unsupervised learning to learn efficient representations of data
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