I fumbled the intro a bit because I started talking about backprop before they even asked about training.
Start with a clear, concise definition of a feed-forward layer as a fully connected layer where information flows in one direction. Then explain its mathematical operation (linear transformation followed by activation) and its role in introducing non-linearity. Finally, connect it to practical considerations like parameter count, computational cost, and trade-offs in system design.
Pro tip: Relate the feed-forward layer to real-world system design by discussing how its size affects latency, memory, and scalability, and mention techniques like quantization or pruning to optimize it for production.
State that it's a fully connected layer where each neuron receives input from all neurons in the previous layer, and information flows forward without cycles.
Describe the linear transformation (weights and biases) followed by a non-linear activation function (e.g., ReLU, sigmoid). Mention that this enables the network to learn complex patterns.
Explain that feed-forward layers are typically used in the final stages of a network for classification or regression, and can be stacked to increase model capacity.
Talk about how the number of layers and neurons affects model performance, training time, inference latency, and memory usage. Mention optimization techniques like pruning, quantization, or using sparse layers.
Relate to Amazon's scale by discussing how feed-forward layers are used in recommendation systems or NLP models, and the importance of efficient inference for customer-facing applications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.