Lilly Tech Systems
Deep Learning Interview Prep
Real interview questions asked at Google, Meta, Amazon, and top AI startups — with detailed model answers and PyTorch code examples. Covers neural network fundamentals, CNNs, RNNs, Transformers, generative models, and training optimization tricks that interviewers love to ask about.
Your Learning Path
Follow these lessons in order for comprehensive interview preparation, or jump to the topic you need to review most.
1. DL Interview Overview
What companies ask in deep learning interviews, the depth expected at each level, how to structure whiteboard explanations, and what distinguishes strong from weak candidates.
2. Neural Network Fundamentals
15 Q&A on activation functions, weight initialization, dropout, batch normalization, skip connections, loss functions, and backpropagation with PyTorch examples.
3. CNN Interview Questions
12 Q&A on convolution operations, pooling, receptive fields, ResNet, VGG, EfficientNet architectures, transfer learning strategies, and feature map computations.
4. RNN & Sequence Models
12 Q&A on vanishing gradients, LSTM gates, GRU, bidirectional RNNs, sequence-to-sequence models, attention mechanisms, and teacher forcing.
5. Transformers & Attention
15 Q&A on self-attention math, multi-head attention, positional encoding, BERT vs GPT, Vision Transformers, scaling laws, and KV-cache optimization.
6. Training & Optimization
12 Q&A on learning rate warmup, mixed precision training, gradient accumulation, data augmentation, knowledge distillation, model pruning, and quantization.
7. Generative Models
10 Q&A on GANs, VAEs, diffusion models, autoregressive generation, evaluation metrics (FID, IS), mode collapse, and classifier-free guidance.
8. Practice Questions & Tips
20 rapid-fire questions, whiteboard drawing tips, common mistakes to avoid, FAQ accordion, and a structured approach to answering any DL interview question.
What You'll Master
By the end of this course, you will be able to:
Explain Any Architecture
Walk through CNNs, RNNs, Transformers, GANs, and diffusion models with the mathematical detail interviewers expect — and know when to simplify.
Write PyTorch from Scratch
Implement self-attention, convolution layers, LSTM cells, and training loops in PyTorch without looking at documentation — a common interview requirement.
Debug Training Issues
Diagnose vanishing gradients, mode collapse, overfitting, and learning rate problems like an experienced ML engineer — the questions that separate senior from junior candidates.
Compare Trade-offs
Articulate why you would choose one architecture, optimizer, or regularization technique over another for a given problem — the hallmark of a strong interview answer.