Activation Function
An activation function is a mathematical function used in neural networks to determine the output of a node.
Actor-Critic
Actor-Critic is a type of reinforcement learning architecture that combines two components: an actor, which makes decisions, and a critic, which evaluates those decisions.
Adaptive Instance Normalization
Adaptive Instance Normalization (AdaIN) is a technique used in neural networks to adjust the style of an image by aligning its feature statistics to a target style.
Advantage Actor-Critic
Advantage Actor-Critic (A2C) is a reinforcement learning algorithm that combines the benefits of both value-based and policy-based methods for optimal decision-making.
Adversarial Example
An adversarial example is a deliberately modified input designed to confuse or mislead machine learning models into making incorrect predictions.
ALBERT
ALBERT is a natural language processing model developed by Google, designed to enhance language understanding with fewer parameters.
AlexNet
AlexNet is a convolutional neural network architecture that revolutionized image classification through deep learning techniques.
AlphaCode
AlphaCode is a sophisticated AI-driven coding assistant developed by DeepMind, designed to help programmers solve complex coding challenges.
AlphaGo
AlphaGo is an artificial intelligence program developed by DeepMind that plays the board game Go at a professional level.
AlphaZero
AlphaZero is an advanced artificial intelligence program developed by DeepMind that uses reinforcement learning to master complex games like chess, shogi, and Go without human input.
ArcFace
ArcFace is a deep learning model designed for face recognition that enhances accuracy by incorporating angular margin into the softmax loss.
Asynchronous Advantage Actor-Critic
(A3C) Short Definition: Asynchronous Advantage Actor-Critic (A3C) is a reinforcement learning algorithm that uses multiple agents running in parallel environments to stabilize and improve learning efficiency.
Attention Mechanism
An attention mechanism is a technique in machine learning that allows a model to focus on the most relevant parts of input data when generating an output.
Autoencoder
An autoencoder is a type of artificial neural network used to learn efficient codings of input data, often for dimensionality reduction.
Average Pooling
Average Pooling is a downsampling operation used in convolutional neural networks (CNNs) to reduce the spatial dimensions of an image by calculating the average value in each sub-region.