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Question 1

What makes deep learning ‘deep’?

Answer 1

c) The number of layers in the network

Question 2

Which of the following is true about convolutional neural networks?

Answer 2

b) They apply filters to detect features in an image

Question 3

What is the main function of the classification module in a ConvNet?

Answer 3

c) To determine the class with the highest confidence score

Question 4

What is an epoch in the context of training a neural network?

Answer 4

c) One full pass through the entire training dataset

Question 5

What drives a ConvNet to converge?

Answer 5

c) Weight adjustments stopping as the network stabilizes

Question 6

Convolutional neural networks are inspired by the Neocognitron, an early model for visual processing.

Answer 6

True

Question 7

The activation maps in ConvNets are similar to activation patterns in the human brain’s visual system.

Answer 7

True

Question 8

The number of layers in a neural network directly reflects the quality of learning, regardless of the network’s architecture.

Answer 8

False

Question 9

In a ConvNet, the highest convolutional layer is used as the final output layer for classification.

Answer 9

False

Question 10

Edge detection typically occurs in the first layer of a ConvNet.

Answer 10

True

Question 11

Why is object recognition challenging for neural networks, despite it seeming easy for humans?

Answer 11

Object recognition is complex for neural networks because they must account for variations in shape, size, orientation, and lighting, whereas the human brain processes visual information with remarkable flexibility and adaptability.

Question 12

Explain the role of convolutional layers in a ConvNet and how they process image data.

Answer 12

Convolutional layers apply filters to the input image, creating activation maps that highlight specific features such as edges, textures, or object parts.

Question 13

What does the term ‘convergence’ mean in neural network training, and why is it important?

Answer 13

Convergence refers to the stabilization of the network’s weights as training progresses.

Question 14

How does backpropagation help a ConvNet learn, and what role do epochs play in this process?

Answer 14

Backpropagation calculates errors from the output layer and adjusts the weights in each layer to minimize these errors.

Question 15

What are some ways ConvNets are similar to the human brain’s visual processing system?

Answer 15

ConvNets process images in a hierarchical manner, where each layer builds upon the previous one to identify increasingly complex features.

Question 16

What was the purpose of the Amazon Mechanical Turk in building the ImageNet dataset?

Answer 16

b) To crowdsource image labeling for training data

Question 17

What is the ‘top-5 accuracy’ metric in the ImageNet competition?

Answer 17

b) The correct label must appear among the model’s top five predictions

Question 18

Why did ConvNets start outperforming other methods in image recognition?

Answer 18

a) Increased access to large datasets and faster parallel computing hardware

Question 19

Which model’s success in the ImageNet competition is considered a turning point for ConvNets?

Answer 19

c) AlexNet

Question 20

What is one of the main challenges ConvNets face in comparison to human object recognition?

Answer 20

b) ConvNets struggle with localization tasks

Question 21

Yann LeCun was a strong advocate for ConvNets during times when the technology was largely overlooked.

Answer 21

True

Question 22

ConvNets are now considered fully human-equivalent in object recognition across all tasks.

Answer 22

False

Question 23

The ImageNet competition used Amazon Mechanical Turk to improve GPU processing speeds.

Answer 23

False

Question 24

ImageNet has 1,000 categories, and models are evaluated using top-5 accuracy.

Answer 24

True

Question 25

Data snooping, seen during some ImageNet competitions, refers to a type of cheating where test data is improperly accessed.

Answer 25

True

Question 26

What were the two key factors that allowed ConvNets to dominate the field of computer vision? Explain how each contributed.

Answer 26

The availability of large datasets (e.g., ImageNet) provided ample training data, allowing models to learn complex features. Additionally, advancements in GPU technology enabled faster parallel computing.

Question 27

Why is top-5 accuracy used as a benchmark in the ImageNet competition, and how does it differ from top-1 accuracy?

Answer 27

Top-5 accuracy is used because it allows the correct label to appear within the top five predictions.

Question 28

Explain why object recognition is not considered ‘solved’ by AI, even though ConvNets perform well on tasks like ImageNet.

Answer 28

Object recognition is challenging for AI because ConvNets still struggle with tasks that require spatial understanding.

Question 29

How did the success of AlexNet in the ImageNet competition influence the development of deep learning and ConvNets?

Answer 29

AlexNet’s success showed that deep ConvNets could outperform traditional methods, leading to a surge in research.

Question 30

What role did the ImageNet competition play in the evolution of computer vision benchmarks, and why is it considered a symbol of progress in AI?

Answer 30

The ImageNet competition provided a clear, quantitative measure of progress in computer vision.

Question 31

Why might ConvNets focus on irrelevant parts of an image, like backgrounds, when classifying objects?

Answer 31

b) They learn patterns based solely on observed data.

Question 32

What does the ‘long tail’ problem refer to in machine learning?

Answer 32

b) Poor performance on rare or unusual examples.

Question 33

What is an adversarial example?

Answer 33

b) An input deliberately modified to mislead an AI model.

Question 34

Which type of AI learning is most suitable for developing general-purpose AI?

Answer 34

b) Unsupervised learning.

Question 35

ConvNets are naturally capable of learning on their own, without the need for labeled data.

Answer 35

False

Question 36

Humans and ConvNets learn in fundamentally different ways, which affects AI’s trustworthiness and robustness.

Answer 36

True

Question 37

The gorilla incident is an example of how biased data can lead to unfair or inaccurate AI predictions.

Answer 37

True

Question 38

Explainable AI (XAI) is designed to make AI decisions easier to understand and interpret.

Answer 38

True

Question 39

ConvNets perform well on non-linearly separable problems without the need for non-linear activation functions.

Answer 39

False

Question 40

How does the way ConvNets learn differ from human learning, and why does this affect their robustness?

Answer 40

Unlike humans, who use context and reasoning, ConvNets learn only from labeled data.

Question 41

Explain the ‘long tail’ problem in supervised learning and why it limits the development of general-purpose AI.

Answer 41

The long tail problem refers to a model’s difficulty with rare or unusual examples.

Question 42

What are adversarial examples, and why do they pose a challenge for ConvNets?

Answer 42

Adversarial examples are inputs that are subtly modified to trick AI models into misclassifying them.

Question 43

Discuss the importance of explainable AI (XAI) and how it might improve trust in AI systems.

Answer 43

Explainable AI aims to make AI decisions more transparent by allowing users to understand the reasons behind predictions.

Question 44

Why might supervised learning result in biased AI systems, and what is a real-world example of this?

Answer 44

Supervised learning can lead to biased AI systems if the training data is unrepresentative of diverse groups.