Table of Contents

Deep‑Learning in Coding Interviews

With no doubt, deep learning is the cornerstone of modern AI systems. For ML engineers, mastering its implementation is not just table stakes—it's what separates standout candidates in AI coding interviews. In this guide we tackle frequent coding challenges on neural-network fundamentals, resurfaced from real interview loops at leading research labs and tech companies. Through hands-on exercises—from tensor operations to complete training loops—you'll refine both mathematical intuition and production-grade implementation skills. Along the way we spotlight interview-critical topics such as autograd systems, optimisation algorithms and numerical-stability techniques, backed by battle-tested patterns.

Core DL Knowledge on Coding

  • Neural Network Basics

  • Implementation Fundamentals

    • • Autograd Systems
    • • Layer Implementation
    • • Optimization Algorithms

  • Training Dynamics

    • • Training Loop Elements
    • • Regularization Techniques
    • • Learning Rate Strategies

  • Engineering Challenges

    • • Computational Efficiency
    • • Numerical Stability

Deep Learning Coding Interview Questions

StatusQuestionCategory
Implement Softmax with Numerical Safeguards
Deep Learning
Implement Xavier Initialization for Neural Network Weights
Deep Learning
Implementing a Multilayer Perceptron (MLP) Layer with Forward and Backward Passes
Deep Learning
Implement Cross-Entropy Loss
Deep Learning
Implement Backward Pass for Tensor Operations
Deep Learning
Implement Stochastic Gradient Descent with Weight Decay and Gradient Accumulation
Deep Learning
Implement Dropout with Train/Test Modes
Deep Learning
Implement a Learning Rate Scheduler with Warmup
Deep Learning
Implement Gradient Clipping in Training Step
Deep Learning

Common Pitfalls in Neural Network Coding

Extended Deep Learning Coding Questions

StatusQuestionCategory
Stabilize Softmax Using Log-Sum-Exp
Efficiency & Numerical Stability
Implement GELU/Swish Activation with Fused Operations
Efficiency & Numerical Stability
Extend SGD with Momentum and Nesterov Acceleration
Scalability & Optimization
Implement Adam Optimizer from Scratch
Scalability & Optimization
Memory-Efficient MLP with Gradient Checkpointing
Engineering Challenges
Implement Mixed-Precision Training Workflow
Engineering Challenges
Implement Second-Order Derivatives for Newton's Method
Advanced Differentiation
Implement AutoGrad System with Basic Operations
Advanced Differentiation
Implement Custom Autograd Functions for Complex Operations
Advanced Differentiation

Debugging Strategies

  1. Debugging setup
    • Start with small-scale tests (single sample/batch, reduced dimensions)
    • Set random seeds for data/weight initialization
    • Test with deterministic algorithms
    • Verify shuffle operations are properly controlled
  2. Verify forward pass step-by-step:
    • Check tensor shapes after each operation
    • Validate intermediate activation outputs
  3. Backward pass validation:
    • Compare manual gradients with numerical gradients (finite difference)
    • Check parameter update magnitudes
    • Verify gradient flow through entire computational graph
  4. Numerical stability checks:
    • Add epsilon guards for divisions/logarithms
    • Monitor for NaN/Inf in forward/backward passes
    • Implement gradient clipping as temporary debug measure
  5. Mode-sensitive debugging:
    • Test train vs. inference modes separately
    • Verify dropout/batchnorm behavior in both modes
    • Check parameter freezing/sharing logic
  6. Gradient checking workflow:
    • Isolate layer/module
    • Compute analytical gradients
    • Compute numerical gradients
    • Compare relative error (<1e-5 good, <1e-3 acceptable)
  7. Edge case testing:
    • Zero-initialized weights
    • All-ones/all-zeros input batches
    • Extreme learning rate values
    • Empty batches/edge batch sizes

Common Follow-ups after Deep Learning Coding Interview

Gradient clipping vs gradient penalty — when to use which?
Clip norms/values during training to prevent exploding gradients in RNNs or GANs; apply a gradient penalty term in the loss (e.g. WGAN-GP) when you need smoother discriminator updates rather than hard truncation.
Batch normalization vs layer normalization?
Batch norm is used for CNNs and RNNs; layer norm is used for transformers and MLPs. Batch norm is used when the batch size is small, while layer norm is used when the batch size is large.
How to debug vanishing gradients in neural networks?
Inspect gradient norms layer-wise; swap sigmoid/tanh for ReLU/LeakyReLU; add residual connections; use He initialisation; and monitor grad.abs().mean() during training to verify flow.
How to apply dropout in train vs eval mode correctly?
During training, zero out activations with probability p and scale survivors by 1 / (1 − p). In evaluation (model.eval()) disable masking so the expected activation stays unchanged.
What's the difference between SGD, Adam, and RMSprop?
SGD uses simple gradient updates; Adam combines momentum with adaptive learning rates using moving averages of gradients and squared gradients; RMSprop adapts learning rates based on moving average of squared gradients without momentum.
How to implement softmax numerically stable?
Use log-sum-exp trick: softmax(x) = exp(x - max(x)) / sum(exp(x - max(x))). Subtract maximum value before exponentiating to prevent overflow, then normalize.
What's the difference between L1 and L2 regularization?
L1 (Lasso) adds λ * sum(|w|) to loss, promotes sparsity; L2 (Ridge) adds λ * sum(w²), prevents large weights. L1 shrinks some weights to exactly zero, L2 shrinks all weights proportionally.
What's the log-sum-exp trick?
For computing log(sum(exp(x))): log(sum(exp(x))) = max(x) + log(sum(exp(x - max(x)))). Prevents overflow by subtracting maximum value before exponentiating.
How to implement gradient checking?
Compare analytical gradients with numerical: (f(x+ε) - f(x-ε))/(2ε). Use small ε (1e-7), check relative error < 1e-7. Useful for debugging custom gradients and verifying autograd.
What's the difference between training and evaluation mode in neural networks?
Training mode: enables dropout, batch norm uses batch statistics, gradients computed; evaluation mode: disables dropout, batch norm uses running statistics, no gradients. Set with model.train() or model.eval().