#Deep Learning

Articles about Deep Learning — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with deep learning. ← All posts

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Pratik Dhanave · ·6 min read

Deep Learning Recommenders

Modern large-scale recommenders — the ones running at the biggest consumer platforms — are built on neural networks. Deep learning didn't replace the core ideas (embeddings, two stages) so much as supercharge them: neural models learn richer embeddings, ingest far more features, and capture complex non-linear patterns that dot products can't. This post covers the two workhorses — two-tower retrieval and neural ranking — that power today's systems.

Modern large-scale recommenders are built on neural networks — not replacing the core ideas (embeddings, two stages) but supercharging them. This post covers the two workhorses: two-tower models for retrieval (the neural evolution of matrix factorization, built for fast ANN search) and rich neural ranking models that score the shortlist with cross-features, sequences, and multiple objectives.

#Recommender Systems#Deep Learning#Machine Learning#AI Engineering#Personalization#Embeddings

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.