Concept graph
Glossary
Short definitions with deeper context and cross-links to sibling terms.
Neural Networks
Activation Function
A mathematical function that determines the output of a neural network layer.
Deep Learning
Artificial Neural Network (ANN)
A computational model inspired by the way biological neural networks function.
deep learning
auto-encoding
A type of artificial neural network used to learn efficient codings of unlabeled data.
neural networks
autoencoder
An autoencoder is a type of neural network used for unsupervised learning of efficient representations.
Training Techniques
Backpropagation
An algorithm used for training neural networks by calculating gradients of the loss function.
Deep Learning
Batch Normalization
A technique to improve training speed and stability in deep neural networks by normalizing the inputs of each layer.
Neural Networks
Convolutional Neural Network (CNN)
A type of deep learning model primarily used for processing grid-like data such as images.
neural-networks
convolutional-encoder
A neural network component that applies convolutional operations to extract features from input data.
neural-networks
convolutional-layer
A layer in a neural network that applies convolution operations to extract features from input data.
Deep Learning
convolutional-neural-network
A class of deep neural networks primarily used for image processing tasks.
Machine Learning
Deep Learning
A subfield of machine learning that uses neural networks with many layers.
Regularization
Dropout
A regularization technique used to prevent overfitting in neural networks by randomly deactivating a fraction of neurons during training.
Deep Learning
Generative Adversarial Network
A class of machine learning frameworks where two neural networks contest with each other to create new data instances.
Neural Networks
Generative Adversarial Network (GAN)
A type of neural network architecture that consists of two competing networks, a generator and a discriminator.
Machine Learning
graph-attention-network
A neural network architecture that employs attention mechanisms to process graph-structured data.
Neural Networks
graph-convolutional-network
A type of neural network designed to process data structured as graphs.
Deep Learning
graph-neural-network
A neural network designed to work directly with graph-structured data.
machine-learning
graph-neural-networks
A type of neural network designed to process data represented as graphs.
Deep Learning
Neural Architecture Search
An automated process for designing neural network architectures optimized for specific tasks.
Machine Learning
Neural Network
A computational model inspired by the way biological neural networks in the human brain work.
hybrid AI
neural-symbolic-integration
A hybrid approach that combines neural networks with symbolic reasoning.
Machine Learning
pruning
The process of removing unnecessary parameters from a neural network to create a more efficient model.
Deep Learning
Recurrent Neural Network (RNN)
A type of neural network designed for sequential data processing.
Deep Learning
recurrent-neural-network
A class of neural networks designed for processing sequences of data.