Regulation of AI
Introduction The regulation of artificial intelligence (AI) refers to the establishment and enforcement of rules, guidelines, and policies that govern the development, deployment, and use of AI technologies. As AI… Regulation of AI
Pseudonymisation
Introduction Pseudonymisation is a data protection technique that involves replacing or encrypting personally identifiable information (PII) with pseudonyms or artificial identifiers. This process aims to enhance privacy and security by… Pseudonymisation
Regression
Introduction Regression is a statistical analysis technique used to model the relationship between a dependent variable and one or more independent variables. It aims to understand and predict the value… Regression
Reinforcement Learning
Introduction Reinforcement learning is a subfield of machine learning that focuses on training an agent to make sequential decisions in an environment. It is inspired by the way humans and… Reinforcement Learning
Self-Supervised Machine Learning
Introduction Self-supervised machine learning is a subfield of artificial intelligence that focuses on training models without the need for explicit human-labeled data. Instead, it leverages the vast amounts of unlabeled… Self-Supervised Machine Learning
Machine Learning Operations (MLOps)
Machine Learning Operations (MLOps): Introduction Machine Learning Operations (MLOps) is a set of practices and techniques that aim to streamline and automate the deployment, management, and monitoring of machine learning… Machine Learning Operations (MLOps)
Local Explainability
Introduction Local explainability refers to the ability to understand and interpret the decisions made by a machine learning model at an individual instance level. It aims to provide insights into… Local Explainability
Operating Point
Introduction An operating point, also known as a bias point or quiescent point, refers to the steady-state condition of an electronic circuit or system. It represents the specific values of… Operating Point
Generative Adversarial Network (GAN)
Introduction A Generative Adversarial Network (GAN) is a type of machine learning model that consists of two neural networks: a generator and a discriminator. The generator network is responsible for… Generative Adversarial Network (GAN)
General Explainability
Introduction General explainability refers to the ability of a system or model to provide understandable and interpretable explanations for its decisions or actions. It is an important aspect of artificial… General Explainability
- « Poprzednie
- 1
- …
- 4
- 5
- 6
- 7
- 8
- …
- 10
- Następne »