Table of Contents

Deep Learning

With no doubt, deep learning is the most important foundation of most modern AI systems. The implementation of deep learning is the most important skill for ML engineers and the key area in AI/ML coding interviews. This blog addresses frequent coding challenges in neural network fundamentals, focusing on practical implementation tasks seen at leading AI research labs and tech companies. Through targeted coding exercises spanning tensor operations to complete training loops, we prepare candidates for whiteboard coding challenges that test both mathematical understanding and production-quality implementation abilities. We will emphasize interview-critical components including autograd systems, optimization algorithms, and numerical stability techniques through battle-tested implementation patterns.

Core Knowledge

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

Key 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

Extended 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