Graph Neural Network
A graph neural network (GNN) is a deep learning architecture designed to operate on graph-structured data like social networks, molecules, and knowledge graphs. GNNs learn by passing messages between connected nodes.
Understanding Graph Neural Network
A graph neural network (GNN) is a deep learning architecture designed to operate on graph-structured data, where entities are represented as nodes and their relationships as edges. Unlike traditional neural networks that expect fixed-size inputs like images or sequences, GNNs learn representations by aggregating information from each node's neighbors through message-passing operations across multiple layers. This makes them naturally suited for social network analysis, molecular property prediction, recommendation systems, traffic forecasting, and knowledge graph reasoning. Popular GNN variants include Graph Convolutional Networks (GCN), GraphSAGE, and Graph Attention Networks that use attention mechanisms similar to those in transformers. GNNs produce node-level, edge-level, or graph-level embeddings that can feed into downstream classification or regression tasks. The architecture addresses problems where relational structure carries critical information that flat feature vectors would lose, bridging deep learning with graph theory and combinatorial optimization.
Category
Deep Learning
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Activation Function
An activation function is a mathematical function applied to a neuron's output to introduce non-linearity into a neural network. Common activation functions include ReLU, sigmoid, and tanh, each with different properties for gradient flow.
Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm that combines the benefits of AdaGrad and RMSProp. It adapts learning rates for each parameter using estimates of first and second moments of gradients.
Adapter Layers
Adapter layers are small trainable modules inserted into a pre-trained model to enable parameter-efficient fine-tuning. They allow task adaptation while keeping the original model weights frozen.
Attention Mechanism
An attention mechanism allows neural networks to focus on the most relevant parts of the input when producing each element of the output. Attention is the foundational innovation behind the Transformer architecture and modern large language models.
Autoencoder
An autoencoder is a neural network trained to compress input data into a compact representation and then reconstruct it. Autoencoders are used for dimensionality reduction, denoising, and learning latent representations.
Backpropagation
Backpropagation is the algorithm used to train neural networks by computing gradients of the loss function with respect to each weight. It propagates error signals backward through the network to update weights and minimize prediction errors.
Batch Normalization
Batch normalization is a technique that normalizes layer inputs across mini-batches during training to stabilize and accelerate neural network training. It reduces internal covariate shift and allows higher learning rates.
Batch Size
Batch size is the number of training examples used in one iteration of gradient descent. Larger batches provide more stable gradient estimates but require more memory, while smaller batches add beneficial noise.