deep-learning - Concepts

Explore concepts tagged with "deep-learning"

Total concepts: 12

Concepts

Attention Mechanism - An AI technique that allows models to focus on relevant parts of input when producing output.
Diffusion Models - Generative AI models that learn to create data by progressively denoising random noise into coherent outputs.
Mixture of Experts - A neural network architecture that uses a gating network to route inputs to specialized sub-networks called experts, enabling efficient scaling by activating only a subset of parameters for each input.
Sparse Models - Neural network architectures where only a fraction of parameters are activated for any given input, enabling larger model capacity with lower computational cost.
Style Transfer - A neural network technique that applies the visual style of one image to the content of another, blending artistic aesthetics with photographic content.
Inpainting - An AI technique for filling in, replacing, or editing selected regions of an image while maintaining visual coherence with the surrounding content.
Text-to-Image - AI technology that generates images from natural language descriptions, translating words into visual content.
Transformer - The neural network architecture underlying modern AI language models.
Multi-Task Learning - A machine learning approach where a single model is trained on multiple related tasks simultaneously, leveraging shared representations to improve generalization.
Model Scaling - The study and practice of increasing neural network size, data, or compute to improve model performance, guided by empirical scaling laws.
Gating Network - A neural network component that learns to route inputs to the most appropriate expert sub-networks in mixture of experts architectures.
Large Language Models (LLMs) - AI models that use transformer architecture to understand and generate human-like text by predicting the next token in a sequence.

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