NSCT-Deep Learning Fundamentals MCQs 20 min Score: 0 Attempted: 0/20 Subscribe 1. . Deep learning is: (A) Compressing datasets only (B) Encrypting neural networks only (C) A subset of machine learning that uses neural networks with multiple layers to model complex patterns (D) Backup onlyShow All Answers 2. . The main advantage of deep learning over traditional machine learning is: (A) Compressing data (B) Encrypting features only (C) Ability to automatically learn features from raw data (D) Backup only 3. . A neuron in a neural network is: (A) Compressing unit only (B) Encrypting unit only (C) A computational unit that receives input, applies weights, sums them, and passes through an activation function (D) Backup only 4. . Weights in a neural network represent: (A) Backup only (B) Encrypting weights (C) Compressing weights (D) The importance of each input in computing the output 5. . Bias in a neuron helps to: (A) Encrypt bias (B) Shift the activation function to better fit the data (C) Compress bias (D) Backup only 6. . Activation functions introduce: (A) Compressing non-linearity (B) Encrypting non-linearity (C) Non-linearity to neural networks allowing them to learn complex patterns (D) Backup only 7. . Common activation functions include: (A) Compressing functions only (B) Encrypting functions only (C) Sigmoid, Tanh, ReLU, Leaky ReLU, and Softmax (D) Backup only 8. . The output layer in a neural network: (A) Encrypts the output (B) Produces the final prediction of the network (C) Compresses the output (D) Backup only 9. . Loss function in deep learning measures: (A) Backup only (B) Encrypting errors (C) Compressing errors (D) The difference between predicted and actual outputs 10. . Common loss functions include: (A) Compressing losses only (B) Encrypting losses only (C) Mean Squared Error (MSE), Cross-Entropy Loss, Hinge Loss (D) Backup only 11. . Backpropagation is: (A) Backup only (B) Encrypting gradients (C) Compressing gradients (D) An algorithm to compute gradients of the loss function with respect to weights for learning 12. . Optimizers in deep learning help to: (A) Backup only (B) Encrypt weights (C) Compress weights (D) Update network weights to minimize the loss function 13. . Common optimizers include: (A) Encrypting optimizers (B) Gradient Descent, Stochastic Gradient Descent (SGD), Adam, RMSprop (C) Compressing optimizers (D) Backup only 14. . Epoch in deep learning is: (A) Encrypting data pass (B) One complete pass of the entire training dataset through the network (C) Compressing epoch (D) Backup only 15. . Batch size refers to: (A) Encrypting batch (B) The number of training samples processed before updating weights (C) Compressing batch (D) Backup only 16. . Overfitting occurs when: (A) Compressing models (B) Encrypting training data (C) The model learns training data too well and fails to generalize on new data (D) Backup only 17. . Dropout in deep learning is used to: (A) Randomly deactivate neurons during training to prevent overfitting (B) Encrypt neurons (C) Compress activations (D) Backup only 18. . Convolutional Neural Networks (CNNs) are mainly used for: (A) Encrypting images (B) Image and video data processing (C) Compressing images (D) Backup only 19. . Recurrent Neural Networks (RNNs) are suitable for: (A) Compressing sequences (B) Encrypting sequences (C) Sequential data such as time series or text (D) Backup only 20. . The main purpose of deep learning fundamentals is to: (A) Compress all features (B) Encrypt all data (C) Build models that can automatically learn complex patterns from data for prediction or classification tasks (D) Backup only
