Deep Learning

Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to progressively extract higher-level features from raw input. It is the foundational technology behind large language models, image generators, speech recognition, and autonomous systems.

The transformer architecture, introduced in 2017, triggered the deep learning revolution that continues to accelerate. The original GPT worked with 110 million parameters; by 2026, frontier models operate with hundreds of billions to over a trillion parameters while becoming dramatically more efficient. The cost of AI inference has dropped 92% in three years, from $30 per million tokens to $0.10-2.50, making deep learning capabilities accessible to individual developers and small teams.

Deep learning now powers virtually every layer of the technology stack. Computer vision models detect objects, generate images, and understand video in real time. Natural language processing models write code, analyze documents, and hold extended conversations. Multimodal models process text, images, audio, and video simultaneously. Foundation models trained on broad datasets can be fine-tuned or prompted for thousands of specialized tasks without retraining.

The frontier has shifted from bigger models to smarter architectures. Mixture-of-experts models activate only relevant subnetworks per query, reducing compute costs. Distillation compresses large model capabilities into smaller, faster versions. On-device deep learning runs on smartphones and smart glasses. The open-source AI movement—led by models like DeepSeek, Llama, and Mistral—has democratized access to frontier-quality deep learning, forcing price competition and accelerating innovation across the entire ecosystem.