Apple ML Interview: Neural Network Architectures Guide

This question examines Neural Network Architectures with a focus on Convolutional Neural Networks (CNNs) and Transformer-based models for images, sequences, and time series.

You will be asked to explain core components (convolutional layers, pooling, activation functions) and Transformer-specific pieces (self-attention, multi-head attention, positional encoding). Be ready to write or reason about the attention score math (e.g., softmax(QK^T / sqrt(d_k))), explain receptive field vs. attention-based context, and describe implementation details such as kernel size, stride, padding, and how to compute FLOPs and memory usage for layers.

Interview flow typically starts high-level (choose an architecture for a task), then drills into design trade-offs (CNN vs. Vision Transformer), followed by math/derivation (attention scaling, positional encodings), and ends with optimization questions (windowed/local attention, sparse or block attention, pruning/quantization) or a short coding/design exercise. You may be asked to adapt architectures for resource constraints (mobile, latency) or data regimes (small dataset, long sequences).

Skill signals you should demonstrate: strong linear algebra intuition, understanding of backprop through convs and attention, practical familiarity with PyTorch/TensorFlow APIs, complexity analysis (time/memory), and the ability to justify architectural choices. Prepare diagrams, complexity estimates (O(n^2) vs O(n)), and concise examples of when to prefer hybrid CNN–Transformer designs.