## 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.

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## 1. When did deep learning start?

- 1943
- 1964
- 1989
- 2005

Answer : A

Explanation: The history of deep learning dates back to 1943 when Warren McCulloch and Walter Pitts created a computer model based on the neural networks of the human brain. So, option A is correct.

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

- 2
- 3
- 5
- Millions

Answer : B

Explanation: Deep learning algorithms are constructed with 3 connected layers : Inner layer, Outer layer and the Hidden layer. So, option B is correct.

## 3. Computers are best at learning:

- Facts
- Concepts
- Procedures
- Principles

Answer : A

Explanation: Computers are best at learning: facts. So, option A is correct.

## 4. A perceptron is:

- A single layer feed-forward neural network with pre-processing
- An auto-associative neural network
- A double layer auto-associative neural network
- A neural network that contains feedback

Answer : A

Explanation: A perceptron is: a single layer feed-forward neural network with pre-processing. It is not an auto-associative network because it has no feedback and is not a multiple-layer neural network because the preprocessing stage is not made of neurons.So, option A is correct.

## 5. **What is the primary goal of deep learning?**

- Data compression
- Feature extraction
- Feature engineering
- Learning patterns from data

Answer : D

Explanation: The primary goal of deep learning is: Learning patterns from data . So, option D is correct.

## 6. Who is the father of deep learning?

- Ilya Sutskever
- Alex Krizhevsky
- Frank Rosenblatt
- David Rumelhart

Answer : C

Explanation: We conclude that Frank Rosenblatt developed and explored all the basic ingredients of the deep learning systems of today, and that he should be recognized as a Father of Deep Learning, perhaps together with Hinton, LeCun and Bengio who have just received the Turing Award as the fathers of the deep learning revolution. So, option C is correct.

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

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

Answer : D

Explanation: In the context of deep learning model deployment, Model drift is 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?**

- To make predictions on new data
- To train the model
- To validate the model
- To fine-tune hyperparameters

Answer : A

Explanation: The primary purpose of model inference in deep learning is to make predictions on new data because “Model inference” is the process of using a trained model to make predictions on new, unseen data.

## 9. RNNs stand for?

- Receives neural networks
- Recording neural networks
- Report neural networks
- Recurrent neural networks

Answer : D

Explanation: RNNs stands for Recurrent Neural Networks : RNN is a multi-layered neural network that can store information in context nodes, allowing it to learn data sequences and output a number or another sequence. So, option D is correct.

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

- Atleast one input attribute
- Atleast one output attribute
- Input attributes to be categorical
- Output attributes to be categorical

Answer : C

Explanation: Supervised learning differs from unsupervised clustering in that supervised learning requires: Input attributes to be categorical.