Python / Python Deep Learning and Neural Networks Interview Questions

What are the differences between Batch Norm, Layer Norm, Group Norm, and Instance Norm?

All normalisation variants compute mean and variance and apply the same transformation (x-μ)/√(σ²+ε) — they differ only in which dimensions the mean and variance are computed over. This seemingly small difference has large practical consequences depending on the architecture and batch size.

Normalisation Comparison
Method	Normalises over	Best for	Key limitation
BatchNorm	Batch + spatial dims per channel	CNNs, large batch MLP	Breaks with batch_size=1; train/eval difference
LayerNorm	All features per sample	Transformers, NLP, RNNs	Slower than BN on large spatial dims
InstanceNorm	Spatial dims per channel per sample	Style transfer, GAN	Loses channel statistics
GroupNorm	Spatial dims per group of channels per sample	Object detection, small batch	Requires choosing n_groups

import torch
import torch.nn as nn

# BatchNorm1d: normalise over batch for FC layers
# Input: (N, C) or (N, C, L)
bn = nn.BatchNorm1d(num_features=128)

# LayerNorm: normalise over feature dim(s) â no dependency on batch
# Input: (*, normalized_shape)  â last dims are normalised
ln = nn.LayerNorm(normalized_shape=128)  # used in transformers
ln_2d = nn.LayerNorm([128, 8, 8])        # can normalise spatial too

# GroupNorm: split channels into groups, normalise per group per sample
# Input: (N, C, *)  â C must be divisible by num_groups
gn = nn.GroupNorm(num_groups=8, num_channels=128)

# InstanceNorm: each sample, each channel independently
inst = nn.InstanceNorm2d(num_features=128)

# Example: why LayerNorm is used in transformers
d_model = 512
x = torch.randn(4, 20, d_model)   # (batch, seq_len, d_model)
# BatchNorm would normalise over batch and seq_len per feature dim â
# unstable at inference when batch=1 (as in autoregressive generation)
# LayerNorm normalises over d_model for each (batch, seq) position independently
print(ln(x).shape)  # (4, 20, 512) â each position normalised independently

Take quiz

Why is LayerNorm preferred over BatchNorm in transformer architectures?LayerNorm is computationally faster than BatchNorm for all input shapes

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LayerNorm normalises each sample independently over its feature dimensions — it has no dependency on batch size, making it consistent at training time, validation, and autoregressive inference with batch_size=1

✓ Correct! Well done.

BatchNorm cannot handle the variable sequence lengths common in NLP

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LayerNorm does not require learnable parameters gamma and beta

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When is GroupNorm particularly useful compared to BatchNorm?GroupNorm is always better than BatchNorm

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GroupNorm is useful in object detection and segmentation where the GPU memory budget forces very small batch sizes — since GroupNorm normalises within each sample rather than across the batch, its estimates are stable even with batch_size=1 or 2

✓ Correct! Well done.

GroupNorm requires larger batch sizes than BatchNorm to function correctly

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GroupNorm is only applicable to 1D sequence data

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Python / Python Deep Learning and Neural Networks Interview Questions

What are the differences between Batch Norm, Layer Norm, Group Norm, and Instance Norm?

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