Deep Learning
A subset of machine learning that uses neural networks with many layers (deep architectures) to automatically learn hierarchical feature representations from raw data.
Deep learning eliminates the need for manual feature engineering by letting the network discover relevant patterns on its own. In a deep image classifier, early layers detect edges, middle layers combine edges into shapes, and later layers recognize objects. This hierarchical learning is what makes deep learning so powerful for unstructured data like images, audio, and text.
The deep learning revolution was enabled by three converging factors: massive datasets (ImageNet, Common Crawl), powerful GPUs that could train large models in reasonable time, and architectural innovations like dropout, batch normalization, and residual connections that made training deep networks stable. Modern LLMs are deep learning models with hundreds of layers and billions of parameters.
For product teams, deep learning is relevant because it powers the AI features users interact with daily. Understanding that these models require large amounts of data, significant compute for training, and careful evaluation helps set realistic expectations. Deep learning models excel at pattern recognition but struggle with reasoning, causality, and tasks requiring explicit knowledge that was not in their training data.
Related Terms
RAG (Retrieval-Augmented Generation)
A technique that grounds LLM responses in external data by retrieving relevant documents at query time and injecting them into the prompt context.
Embeddings
Dense vector representations of text, images, or other data that capture semantic meaning in a high-dimensional space, enabling similarity search and clustering.
Vector Database
A specialized database optimized for storing, indexing, and querying high-dimensional vector embeddings with sub-millisecond similarity search.
LLM (Large Language Model)
A neural network trained on massive text corpora that can generate, understand, and transform natural language for tasks like summarization, classification, and conversation.
Fine-Tuning
The process of further training a pre-trained LLM on a domain-specific dataset to specialize its behavior, style, or knowledge for a particular task.
Prompt Engineering
The practice of designing and iterating on LLM input instructions to reliably produce desired outputs for a specific task.