Course ML Software Engineering: Interview Concept Review Chapter 13 Difficulty intermediate Estimated Time 900 min

Chapter 13: Deep Learning I — Vision & Architectures

Deep Learning I — Vision & Architectures in ML Software Engineering: Interview Concept Review.

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Learning Objectives

By the end of this chapter, you will be able to:

  • Relate Deep Learning I — Vision & Architectures to common ML software engineering interview questions and trade-offs.
  • Explain when this topic deserves a deeper pass through another tutorial on this site versus staying at recap depth.
  • Surface assumptions, pitfalls, and follow-up probes an interviewer is likely to use.

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Fully-connected networks

Composable affine maps + nonlinearities approximate functions; universal approximation story—fine for intuition—but depth helps hierarchical features at cost of optimization difficulty.

Activations: ReLU cheap but dying units; leaky/Swish mitigate partially; interviewer may mention saturation of sigmoid/tanh historically.

Initialization: zero weights → symmetry lock; Xavier/He scales preserve forward variance early.

Convolutional biases

Local connectivity + parameter sharing encode translation equivariance. Valid vs same padding: maintain spatial size vs shrink—articulate interplay with stride and receptive field growth across layers.

Detection families (survey): two-stage propose-then-refine vs one-stage speed—accuracy/latency trade for video interviews.

Autoencoders, VAE sketch, GAN positioning

Autoencoder: bottleneck forces compression; reconstruction error flags anomalies.

VAE: latent regularized via KL to prior enabling generative sampling with crisp ELBO language optional.

GAN: adversarial minimax game yields sharp samples but unstable training.

DL limitations: sample complexity, brittle OOD behaviour, infra cost—pair with evaluation beyond aggregate accuracy slices.

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Neural Networks Fundamentals

1. Same padding primarily: