[Interview question for Computer vision] Part 3: Convolution Neural Networks-Based Interview Questions & Answers
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Some interview questions and answers for the computer vision field. Part 3 shows some convolution neural networks questions and answers.
Convolution Neural Networks-Based Interview Questions & Answers
What is a Convolutional Neural Network (CNN)?
Answer: A Convolutional Neural Network (CNN) is a type of deep learning neural network specifically designed for processing grid-like data, such as images and videos. It uses a series of convolutional layers to automatically and adaptively learn spatial hierarchies of features from the input data.
What is the purpose of Convolutional Layers in a CNN?
Answer: Convolutional Layers apply filters (kernels) to the input data to detect specific features like edges, textures, or patterns. These filters slide across the input data to generate feature maps that capture hierarchical information.
Explain the concept of Pooling in CNNs.
Answer: Pooling is a downsampling operation that reduces the spatial dimensions of the feature maps while retaining the most important information. Max pooling, for example, takes the maximum value from a region of the feature map, effectively reducing its size.
What is the purpose of Activation Functions in CNNs?
Answer: Activation functions introduce non-linearity into the model, allowing it to learn and approximate complex, non-linear relationships in the data. Common activation functions in CNNs include ReLU (Rectified Linear Unit), Sigmoid, and Tanh.
Explain the concept of Fully Connected Layers in a CNN.
Answer: Fully Connected Layers, also known as dense layers, connect every neuron from the previous layer to every neuron in the current layer. These layers are typically found at the end of a CNN and are responsible for making final decisions based on the extracted features.
What is the purpose of Stride in a Convolutional Layer?
Answer: Stride in a Convolutional Layer determines how much the filter moves across the input data. A larger stride leads to a smaller output size, as the filter skips more pixels. This can help in reducing the spatial dimensions and computational complexity.
What is Dropout and why is it used in CNNs?
Answer: Dropout is a regularization technique used in neural networks, including CNNs, to prevent overfitting. It randomly sets a fraction of the neurons to zero during training, effectively “dropping out” some information. This forces the network to be more robust and prevents it from relying too heavily on specific neurons.
What is Batch Normalization and why is it important in CNNs?
Answer: Batch Normalization is a technique used to stabilize and accelerate the training of neural networks. It normalizes the activations of each layer in a mini-batch, reducing internal covariate shift. This leads to faster convergence and allows for higher learning rates.
What is Transfer Learning in the context of CNNs?
Answer: Transfer Learning is a technique where a pre-trained CNN, typically on a large dataset, is used as a starting point for a new task. Instead of training a model from scratch, the existing knowledge from the pre-trained network is fine-tuned on a smaller dataset specific to the new task.
Explain the concept of Data Augmentation in CNNs.
Answer: Data Augmentation involves applying various transformations to the training data, such as rotations, flips, zooms, and translations. This artificially increases the diversity of the training set, which can lead to a more robust and accurate model.