Results for "deep learning"
Deep Learning
IntermediateA branch of ML using multi-layer neural networks to learn hierarchical representations, often excelling in vision, speech, and language.
Deep Learning is a type of machine learning that uses structures called neural networks, which are inspired by how the human brain works. Imagine a series of layers where each layer learns to recognize different features of an image, like edges, shapes, and eventually, whole objects. This is how ...
Inferring and aligning with human preferences.
A subfield of AI where models learn patterns from data to make predictions or decisions, improving with experience rather than explicit rule-coding.
A structured collection of examples used to train/evaluate models; quality, bias, and coverage often dominate outcomes.
Human or automated process of assigning targets; quality, consistency, and guidelines matter heavily.
System design where humans validate or guide model outputs, especially for high-stakes decisions.
Balancing learning new behaviors vs exploiting known rewards.
Systematic error introduced by simplifying assumptions in a learning algorithm.
Continuous cycle of observation, reasoning, action, and feedback.
Loss of old knowledge when learning new tasks.
Combining simulation and real-world data.
RL without explicit dynamics model.
Learned model of environment dynamics.
Learning without catastrophic forgetting.
A measure of a model class’s expressive capacity based on its ability to shatter datasets.
A measurable property or attribute used as model input (raw or engineered), such as age, pixel intensity, or token ID.
A function measuring prediction error (and sometimes calibration), guiding gradient-based optimization.
Practices for operationalizing ML: versioning, CI/CD, monitoring, retraining, and reliable production management.
Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.
Hidden behavior activated by specific triggers, causing targeted mispredictions or undesired outputs.
Reconstructing a model or its capabilities via API queries or leaked artifacts.
Reduction in uncertainty achieved by observing a variable; used in decision trees and active learning.
All possible configurations an agent may encounter.
Models evaluating and improving their own outputs.
Increasing performance via more data.
Optimization under uncertainty.
Model trained on its own outputs degrades quality.
RL using learned or known environment models.
Predicts next state given current state and action.
Learning action mapping directly from demonstrations.
Identifying suspicious transactions.