deep-learning - Concepts

Explore concepts tagged with "deep-learning"

Total concepts: 17

Concepts

Multi-Task Learning - A machine learning approach where a single model is trained on multiple related tasks simultaneously, leveraging shared representations to improve generalization.
Attention Mechanism - An AI technique that allows models to focus on relevant parts of input when producing output.
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.
Text-to-Image - AI technology that generates images from natural language descriptions, translating words into visual content.
Autoencoder - A neural network architecture that learns compressed representations by encoding input into a lower-dimensional latent space and then decoding it back to reconstruct the original input.
Transformer - The neural network architecture underlying modern AI language models.
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.
Model Scaling - The study and practice of increasing neural network size, data, or compute to improve model performance, guided by empirical scaling laws.
Generative Adversarial Network - A machine learning framework where two neural networks compete against each other — a generator creating synthetic data and a discriminator evaluating its authenticity — to produce increasingly realistic outputs.
Variational Autoencoder - A generative model that learns a structured, continuous latent space by combining autoencoder architecture with probabilistic inference, enabling generation of new data by sampling from the learned distribution.
Inpainting - An AI technique for filling in, replacing, or editing selected regions of an image while maintaining visual coherence with the surrounding content.
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.
Representation Learning - A class of machine learning techniques where models automatically discover the representations needed for a task from raw data, rather than relying on manually engineered features.
Diffusion Models - Generative AI models that learn to create data by progressively denoising random noise into coherent outputs.
Gating Network - A neural network component that learns to route inputs to the most appropriate expert sub-networks in mixture of experts architectures.
Backpropagation - The fundamental algorithm for training neural networks that efficiently computes gradients of the loss function with respect to each weight by propagating errors backward through the network layers.
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.

← Back to all concepts