Introduction to Deep Learning for Computer Vision certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Introduction to Deep Learning for Computer Vision exam and earn Introduction to Deep Learning for Computer Vision certificate.
Table of Contents
Question 1
What is the primary difference between machine learning and deep learning?
A. Machine learning is only used for numerical data and cannot be used for images
B. Machine learning automatically extracts trainable features
C. Deep learning is a subset of machine learning well-suited to complex analyses
Answer
C. Deep learning is a subset of machine learning well-suited to complex analyses
Explanation
Deep learning is a specialized subset of machine learning designed to handle highly complex data and patterns, particularly unstructured data such as images, audio, and video. While traditional machine learning often relies on manually engineered features provided by domain experts, deep learning uses multi-layer neural networks to automatically learn hierarchical feature representations directly from raw data, making it especially effective for complex analytical tasks.
Question 2
How do Convolutional Neural Networks extract useful information from images?
A. By ignoring spatial information
B. By performing convolution to learn and extract features
C. By analyzing each pixel independently of neighboring pixels
Answer
B. By performing convolution to learn and extract features
Explanation
Convolutional Neural Networks extract useful information by applying convolution operations that slide learnable filters across an image to detect meaningful patterns such as edges, textures, and shapes. These filters capture local spatial relationships between pixels, allowing the network to progressively learn increasingly abstract visual features as data moves through deeper layers.
Question 3
What is the purpose of activation layers in CNNs?
A. To reduce the size of outputs
B. To identify and forward important information
C. To learn complex relationships between features
Answer
C. To learn complex relationships between features
Explanation
Activation layers introduce non-linearity into the network, enabling CNNs to learn complex relationships between extracted features. Without activation functions, the network would behave like a linear model and fail to represent real-world image patterns, which are inherently non-linear. Common activations such as ReLU help the model focus on relevant signals while maintaining computational efficiency.
Question 4
Which of the following describes the function of a pooling layer?
A. It performs convolution to learn and extract features
B. It adds noise to the data
C. It reduces the size of outputs
Answer
C. It reduces the size of outputs
Explanation
Pooling layers reduce the spatial size of feature maps by summarizing local regions, typically using operations such as max pooling or average pooling. This reduction lowers computational cost, controls overfitting, and helps the network become more robust to small spatial variations in the input, such as shifts or distortions in images.
Question 5
Why do CNNs repeat convolution, activation, and pooling layers many times?
A. To slow down the training process
B. To eliminate the need for fully connected layers
C. To capture complex features
Answer
C. To capture complex features
Explanation
CNNs repeat these layers to progressively capture more complex and abstract features from images. Early layers learn simple patterns like edges, while deeper layers combine these patterns to recognize higher-level structures such as objects or faces. This hierarchical feature learning is essential for strong performance in computer vision tasks.