Convolutional Neural Networks (CNNs) have become a fundamental concept in machine learning, particularly in the field of computer vision. They have revolutionized tasks such as image classification, object detection, and segmentation, achieving state-of-the-art performance on many benchmark datasets. CNNs have been deployed in various real-world applications, from self-driving cars to medical image analysis, making them an essential tool for any machine learning practitioner.

This interview will cover the key aspects of CNNs, including:

• Convolutional layers and their role in feature extraction
• Pooling layers and their importance in downsampling and translation invariance
• Well-known CNN architectures and their contributions to the field
• Regularization techniques, such as dropout and data augmentation, to prevent overfitting
• Applications of CNNs in computer vision and beyond

By the end of this interview, the candidate will have a solid understanding of the fundamental concepts behind CNNs, their architectures, and their applications. They will be well-prepared to tackle technical questions related to CNNs and demonstrate their expertise in this crucial area of machine learning.