Results for "trial-and-error"

A conceptual framework describing error as the sum of systematic error (bias) and sensitivity to data (variance).

A function measuring prediction error (and sometimes calibration), guiding gradient-based optimization.

A proper scoring rule measuring squared error of predicted probabilities for binary outcomes.

Measures a model’s ability to fit random noise; used to bound generalization error.

Systematic error introduced by simplifying assumptions in a learning algorithm.

Error due to sensitivity to fluctuations in the training dataset.

Learning by minimizing prediction error.

Average of squared residuals; common regression objective.

The field of building systems that perform tasks associated with human intelligence—perception, reasoning, language, planning, and decision-making—via algori...

The relationship between inputs and outputs changes over time, requiring monitoring and model updates.

Separating data into training (fit), validation (tune), and test (final estimate) to avoid leakage and optimism bias.

Networks using convolution operations with weight sharing and locality, effective for images and signals.

Architecture based on self-attention and feedforward layers; foundation of modern LLMs and many multimodal models.

Processes and controls for data quality, access, lineage, retention, and compliance across the AI lifecycle.

Policies and practices for approving, monitoring, auditing, and documenting models in production.

Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.

Logging hyperparameters, code versions, data snapshots, and results to reproduce and compare experiments.

Tracking where data came from and how it was transformed; key for debugging and compliance.

Observing model inputs/outputs, latency, cost, and quality over time to catch regressions and drift.

Converts logits to probabilities by exponentiation and normalization; common in classification and LMs.

A system that perceives state, selects actions, and pursues goals—often combining LLM reasoning with tools and memory.

Identifying and localizing objects in images, often with confidence scores and bounding rectangles.

AI subfield dealing with understanding and generating human language, including syntax, semantics, and pragmatics.

AI systems that perceive and act in the physical world through sensors and actuators.

A branch of ML using multi-layer neural networks to learn hierarchical representations, often excelling in vision, speech, and language.

A learning paradigm where an agent interacts with an environment and learns to choose actions to maximize cumulative reward.

Learning where data arrives sequentially and the model updates continuously, often under changing distributions.

A mismatch between training and deployment data distributions that can degrade model performance.

A structured collection of examples used to train/evaluate models; quality, bias, and coverage often dominate outcomes.

When a model fits noise/idiosyncrasies of training data and performs poorly on unseen data.