NSCT-Advanced Deep Learning MCQs 20 min Score: 0 Attempted: 0/20 Subscribe 1. . Advanced deep learning focuses on: (A) Compressing datasets only (B) Encrypting neural networks (C) Complex architectures and techniques such as CNNs, RNNs, LSTMs, GANs, and Transformers (D) Backup onlyShow All Answers 2. . Convolutional Neural Networks (CNNs) are best suited for: (A) Backup only (B) Encrypting images (C) Compressing image data (D) Image recognition, object detection, and computer vision tasks 3. . Key layers in CNN include: (A) Encrypting layers (B) Convolutional layer, pooling layer, fully connected layer (C) Compression layers (D) Backup only 4. . Pooling layers in CNNs are used to: (A) Encrypt pooled data (B) Reduce spatial dimensions and computational complexity while retaining important features (C) Compress features (D) Backup only 5. . Recurrent Neural Networks (RNNs) are suitable for: (A) Compressing sequences (B) Encrypting sequences (C) Sequential data such as text, speech, and time series (D) Backup only 6. . Long Short-Term Memory (LSTM) networks help to: (A) Compress sequences (B) Encrypt memory (C) Solve the vanishing gradient problem in RNNs and capture long-term dependencies (D) Backup only 7. . Gated Recurrent Units (GRUs) are: (A) Compressing GRUs (B) Encrypting GRUs (C) Simplified versions of LSTMs with fewer parameters (D) Backup only 8. . Generative Adversarial Networks (GANs) consist of: (A) A generator and a discriminator network competing in a zero-sum game (B) Encrypting GANs (C) Compressing GANs (D) Backup only 9. . GANs are mainly used for: (A) Data generation, image synthesis, and augmentation (B) Encrypting images (C) Compressing datasets (D) Backup only 10. . Autoencoders are used for: (A) Compressing features only (B) Encrypting data (C) Dimensionality reduction, denoising, and unsupervised feature learning (D) Backup only 11. . Attention mechanism in deep learning helps to: (A) Encrypt attention weights (B) Focus on important parts of input sequences for better prediction (C) Compress sequences (D) Backup only 12. . Transformers are: (A) Backup only (B) Encrypting transformers (C) Compressing transformers (D) Deep learning architectures that rely entirely on attention mechanisms, widely used in NLP 13. . BERT (Bidirectional Encoder Representations from Transformers) is used for: (A) Encrypting text (B) NLP tasks such as text classification, question answering, and sentiment analysis (C) Compressing text (D) Backup only 14. . Transfer learning in deep learning involves: (A) Backup only (B) Encrypting weights (C) Compressing models (D) Using a pre-trained model on a new but related task to save time and improve performance 15. . Fine-tuning is: (A) Adjusting weights of a pre-trained model on new data to adapt it to a specific task (B) Encrypting models (C) Compressing weights (D) Backup only 16. . Dropout in advanced deep learning is used to: (A) Backup only (B) Encrypt neurons (C) Compress layers (D) Prevent overfitting by randomly deactivating neurons during training 17. . Batch normalization helps to: (A) Encrypt batches (B) Accelerate training and stabilize learning by normalizing layer inputs (C) Compress layers (D) Backup only 18. . Residual connections in deep networks: (A) Encrypt residuals (B) Help train very deep networks by allowing gradients to flow directly (C) Compress layers (D) Backup only 19. . Hyperparameter tuning in deep learning involves: (A) Selecting optimal values for learning rate, batch size, number of layers, and neurons (B) Encrypting hyperparameters (C) Compressing hyperparameters (D) Backup only 20. . The main purpose of advanced deep learning is to: (A) Solve complex tasks in vision, language, and sequential data using sophisticated neural network architectures (B) Encrypt all models (C) Compress all features (D) Backup only
