Deep Learning

What is Deep Learning?

Deep learning is a subset of machine learning that uses neural networks with many layers to learn complex patterns from large datasets. It excels at language, vision, and sequence tasks that traditional models struggle with.

How It Works (quick)

• Neural networks: Stacks of layers transform inputs into higher-level features.
• Training: Optimize weights by minimizing loss with gradient descent.
• Architectures: CNNs (images), RNNs/Transformers (text & sequences), autoencoders (compression), and two-tower models (retrieval).
• Inference: Use trained models to score, classify, retrieve, or generate outputs.

Why It Matters in E-commerce

• Search relevance: Semantic embeddings and re-ranking (e.g., BERT, dual-encoders).
• Recommendations: Learn user/item representations from behavior.
• Image tasks: Visual similarity, auto-tagging, quality checks.
• Operations: Demand forecasting, fraud detection, pricing.

Best Practices

• Start hybrid: lexical baseline + deep re-ranker.
• Latency budgets: Quantize/distill; cache embeddings; batch requests.
• Governance: Version models; monitor drift; add guardrails.
• Data quality: Clean attributes, de-dup, balance classes.
• Measure: NDCG/MRR/CTR/conv, plus cost and latency.

Challenges

• Compute cost, cold start, bias, explainability, and model drift.

Examples

• Semantic retrieval for “vegan leather tote with zipper.”
• Vision-based “similar products” on PDP.
• Next-best-offer using session embeddings.

Summary

Deep learning adds powerful semantics and perception to commerce systems. With the right hybrid design and guardrails, it boosts relevance and recommendations under real-world latency.