Deep Learning
Deep learning is machine learning using neural networks with many layers. Depth allows models to learn hierarchical representations and has driven breakthroughs in vision, language, and other domains.
In Simple Terms
Think of it as a multi-story factory: raw input enters at the bottom and each floor adds another level of abstraction until the top produces the final product.
Detailed Explanation
Deep learning excels when you have large amounts of data and compute. Applications include image recognition, machine translation, speech, and generative models. Training typically requires GPUs or other accelerators and careful tuning of architecture and hyperparameters. The field continues to evolve with larger models, new architectures, and better training methods. Deep learning is now the default approach for most perception and generation tasks in industry and research.
Related Terms
Context Engineering
Context engineering is the practice of structuring and managing context—including system prompts, RAG (Retrieval-Augmented Generation), memory, and few-shot examples—so AI systems have the right information to answer accurately.
Read moreNatural Language Processing
Technology that helps computers understand, interpret, and manipulate human language.
Read moreRAG
Retrieval-Augmented Generation combines AI models with external knowledge retrieval for accurate responses.
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