The following quiz “Deep Learning MCQ Questions And Answers” provide Multiple Choice Questions (MCQs) related to Deep Learning. These Deep Learning MCQs are also Interviews (campus interview, walk-in interview, company interview), Placement or recruitment, entrance examinations, and competitive examinations oriented. You can practice the below questions to improve your Deep Learning skills. You can click on the View Answer button to check the answer. Let’s solve this Deep Learning MCQ Questions And Answers Quiz. Deep learning is a method in artificial intelligence (AI) that simulates our brain, helping systems learn to identify objects and perform complex tasks with increasing accuracy without human intervention. Deep Learning is the subset of Machine Learning (ML) that involves the use of neural networks to model and solve complex problems. The adjective “deep” in Deep Learning refers to the use of multiple layers in the network.

📚 Table of Contents:

• Pages 1–3 (Q1–Q30): Deep learning origin (1943), DL layers (3), computers learn facts, perceptron definition, DL primary goal, father of DL (Frank Rosenblatt), model drift, model inference purpose, RNNs full form, supervised vs unsupervised difference, auto-associative network, CNN for image classification, canary deployment, RNN use cases, 1×1 convolutions, dependent variable, deep vs shallow NN benefit, latency in deployment, DL limitations, DL value example, shallow NN (one hidden layer), Infer.net visualization, CNN for perceptual tasks, input-hidden connections (10→5=50), pre-trained model improvement (transfer learning), A/B testing purpose, convolution output size, CNN for unstructured data, DL accuracy vs ML, loss function role
• Page 4 (Q31–Q40): DL vs ML differences, backpropagation, vanishing gradient problem and solutions, activation functions role, batch normalization, CNN vs RNN architecture, Transformer architecture, LSTM and its purpose, GRU vs LSTM, attention mechanism
• Page 5 (Q41–Q50): GANs, autoencoders, dropout regularization, overfitting/underfitting, PyTorch vs TensorFlow, ResNet, object detection architectures, semantic segmentation, NLP and deep learning, word embeddings
• Page 6 (Q51–Q60): BERT vs GPT, transfer learning, convolutional layer vs pooling layer, data augmentation, epochs/batch size/iterations, loss function vs optimizer, DL in computer vision, convolution operation, image classification vs recognition, DL in speech recognition
• Page 7 (Q61–Q70): RLHF, variational autoencoder (VAE), max vs average pooling, model compression, fine-tuning, embedding layer, DL in healthcare, DL frameworks, Generative AI, diffusion models
• Page 8 (Q71–Q80): Deep reinforcement learning, exploding gradient, DL in autonomous vehicles, “Attention Is All You Need” paper, supervised vs unsupervised vs self-supervised, learning rate schedules, weight initialization, contrastive learning, knowledge distillation, Neural Architecture Search
• Page 9 (Q81–Q90): Natural language generation, mixed precision training, encoder-decoder architecture, hyperparameter tuning, gradient descent variants, GPUs in DL, neural network interpretability, federated learning, multi-task learning, training vs evaluation
• Page 10 (Q91–Q100): Adversarial examples, NeRF, model parallelism, discriminative vs generative models, curriculum learning, zero-shot/few-shot learning, representation learning, graph neural networks, scaling laws, current DL trends and future directions

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1. When did deep learning start?

1. 1943
2. 1964
3. 1989
4. 2005

2. How many layers Deep learning algorithms are constructed with?

1. 2
2. 3
3. 5
4. Millions

3. Computers are best at learning:

1. Facts
2. Concepts
3. Procedures
4. Principles

4. A perceptron is:

1. A single layer feed-forward neural network with pre-processing
2. An auto-associative neural network
3. A double layer auto-associative neural network
4. A neural network that contains feedback

5. What is the primary goal of deep learning?

1. Data compression
2. Feature extraction
3. Feature engineering
4. Learning patterns from data

6. Who is the father of deep learning?

1. Ilya Sutskever
2. Alex Krizhevsky
3. Frank Rosenblatt
4. David Rumelhart

7. In the context of deep learning model deployment, what is “model drift”?

1. A process of updating model weights continuously
2. A technique for making models faster
3. A method for quantizing model parameters
4. A phenomenon where the model’s performance degrades over time due to changes in data distribution

8. What is the primary purpose of model inference in deep learning?

1. To make predictions on new data
2. To train the model
3. To validate the model
4. To fine-tune hyperparameters

9. RNNs stand for?

1. Receives neural networks
2. Recording neural networks
3. Report neural networks
4. Recurrent neural networks

10. Supervised learning differs from unsupervised clustering in that supervised learning requires

1. Atleast one input attribute
2. Atleast one output attribute
3. Input attributes to be categorical
4. Output attributes to be categorical