Compare CNN/RNN/LSTM and implement K-means

Q: Compare CNN/RNN/LSTM and implement K-means

This is a Machine Learning interview question from Microsoft for Data Scientist roles. View the full question and solution on PracHub.

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Question

Deep Learning Concepts and K-means Implementation (Onsite ML Interview)

Part A: CNNs vs RNNs and LSTMs

Contrast CNNs and RNNs for the following modalities:

(i) 224×224 RGB images
(ii) Variable-length text

Explain and compare:

Inductive biases: translation equivariance, locality (spatial/temporal), temporal order
Parameter sharing
Receptive-field growth
Ability to model long-range dependencies
When a 1D CNN can replace an RNN/Transformer (assumptions and caveats)

For LSTMs:

Write the gate equations and define all symbols and shapes
Show mathematically how the cell state helps mitigate vanishing gradients
Compute output shapes for input (batch=32, seq_len=100, feat=64), hidden size=128 for unidirectional vs bidirectional cases (assume a single layer)

Part B: K-means From Scratch

Implement K-means with the following requirements:

k-means++ initialization
Vectorized assignment and update steps
Convergence based on decrease of the objective (sum of squared distances)

Also discuss:

Time complexity O(n·k·d) per iteration and memory trade-offs
Handling empty clusters
Feature scaling and outliers
Choosing k (silhouette, elbow, BIC)
Prove the objective is non-increasing per iteration
Propose a mini-batch variant and when you would use it

Compare CNN/RNN/LSTM and implement K-means

Deep Learning Concepts and K-means Implementation (Onsite ML Interview)

Part A: CNNs vs RNNs and LSTMs

Part B: K-means From Scratch

Solution (Locked)

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