I started with spatial locality and parameter sharing, which felt right, but I fumbled a bit explaining why fully connected layers are actually bad for images at scale.
Start by defining the core architectural differences between CNNs and traditional fully connected networks, emphasizing how CNNs exploit spatial locality and parameter sharing. Then explain how these differences lead to practical advantages in image processing, such as reduced parameters, translation invariance, and hierarchical feature learning. Finally, connect these advantages to real-world applications, like Netflix's content understanding or recommendation systems.
Pro tip: Tie your answer to Netflix's business context—mention how CNNs enable efficient image-based recommendations or content tagging, showing you understand the company's needs beyond just theory.
Explain that traditional networks (MLPs) treat inputs as flat vectors, ignoring spatial structure, and connect every neuron to every input, leading to a huge number of parameters for images.
Describe how CNNs use convolutional layers with local receptive fields, shared weights, and pooling to preserve spatial relationships and reduce parameters.
Contrast how MLPs flatten images and lose spatial information, while CNNs process images in their original 2D/3D form, capturing local patterns like edges and textures.
Discuss benefits: parameter efficiency, translation invariance, hierarchical feature extraction, and better generalization for image tasks.
Connect to Netflix applications such as thumbnail personalization, content classification, or visual recommendation systems, showing practical impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.