Winner-take-all in action selection
Definition
Winner-take-all (WTA) is a competitive process used in neural networks and reinforcement …
Neural Networks
Terms tagged with Neural Networks
Sigmoid
Definition
The sigmoid function, defined as σ(z) = 1 / (1 + e^-z), is widely used in machine …
Parity Learning
Definition
Parity Learning is a benchmark problem in machine learning theory where the goal is to …
Neural computation
Definition
Neural computation refers to the mathematical operations performed by artificial neurons …
Layer Normalization
Definition
Layer Normalization stabilizes training by reducing internal covariate shift, …
Highway network
Definition
Highway Networks are designed to address the vanishing gradient problem in deep learning …
Hidden Layer
Definition
A hidden layer consists of neurons that receive inputs from previous layers, apply …
Gated Recurrent Unit
Definition
A Gated Recurrent Unit (GRU) is a specialized recurrent neural network (RNN) cell …
Feed-Forward Network
Definition
Feed-Forward Networks (FFNs), also known as Multi-Layer Perceptrons (MLPs), process data …
Epoch
Definition
In machine learning, an epoch represents a single iteration over the entire training …
Dense
Definition
In neural networks, ‘dense’ refers to fully connected layers where each …
Continual Learning
Definition
Continual learning, also known as lifelong learning, enables neural networks to acquire …
Batch Normalization
Definition
This method adjusts and scales activations to have zero mean and unit variance within …
Actor-critic algorithm
Definition
The actor-critic algorithm employs two components: the actor, which updates the policy to …
A Logical Calculus of the Ideas Immanent in Nervous Activity
Definition
This foundational paper proposed a mathematical model of neural networks, demonstrating …
Recurrent Neural Network
Definition
RNNs are designed to recognize patterns in sequences of data, such as text, genomes, …
ReLU
Definition
ReLU is widely used in deep learning neural networks due to its computational efficiency …
Softmax
Definition
Softmax is widely used in the output layer of neural networks for multi-class …
Decoder
Definition
In sequence-to-sequence models, the decoder takes the context vector produced by the …
Activation Function
Definition
An activation function introduces non-linearity into a neural network, allowing it to …
Linear
Definition
Linear operations involve multiplication and addition without non-linear activations. In …
Convolutional Neural Network
Definition
Convolutional Neural Networks (CNNs) are designed to automatically and adaptively learn …
Deep Learning
Definition
Deep learning algorithms attempt to mimic the human brain’s analytical and learning …
Backpropagation
Definition
Backpropagation, short for backward propagation of errors, is a method used in artificial …