Deep Learning
AI & Machine LearningA subset of machine learning that trains multi-layered neural networks to learn complex representations from data, the core technique behind modern AI.
Deep learning is the primary workload Eleveight AI's infrastructure is built to support, from large-scale training runs to production inference serving, on the same B300 hardware.
Overview
Deep learning uses neural networks built from many stacked layers that learn to represent data hierarchically. Early layers pick out simple, low-level features; each successive layer combines what came before into something more abstract, until the network captures patterns far too intricate to specify by hand. The word "deep" refers simply to that depth, the sheer number of layers.
How it works
The network is shown labeled examples and produces a prediction. An error, or loss, is measured against the correct answer, and an algorithm called backpropagation works backward through the layers, adjusting each weight slightly to reduce that error. Repeated across millions or billions of examples, this gradually shapes the network, and GPU parallelism is what makes the repetition feasible.
Why it matters
Deep learning is the engine beneath nearly every modern AI capability, the large language models, the image generators, the speech systems, and much else. Its defining trait is that it learns the relevant features directly from data rather than relying on humans to engineer them, which is precisely what unlocked progress on problems that resisted earlier approaches.
Use cases
- Training large language models (LLMs)
- Image and video classification and generation
- Speech recognition and synthesis
- Pharmaceutical discovery
- Recommendation systems