neural networks

Machine learning architecture

Neural networks are computing systems modeled loosely on the human brain, using interconnected nodes that process information in layers.

They learn patterns from data rather than following explicit instructions, making them central to modern artificial intelligence: The neural network was trained on millions of images.

Deep neural networks have many layers, enabling recognition of complex patterns in speech, images, and text.

architecture

layers

Stacked levels or coatings

  • nodesPoints of connection
  • weightsHeavy objects for lifting
  • biasUnfair inclination or prejudice

perceptron · convolutional · recurrent · feedforward · activation function · hidden layers · transformer · GAN · encoder · decoder

training

backpropagation

Reverse gradient computation method

overfitting · underfitting · regularization · supervised · unsupervised · reinforcement · generalization · validation · hyperparameters · convergence

biological

synapses

Neuron-to-neuron transmission points

  • axonsNeuron output extensions
  • dendritesNeuron's signal-receiving projections
  • cortexOrgan's external tissue

hippocampus · cerebellum · neurotransmitters · glia · brainwaves · amygdala · neuroplasticity · grey matter · white matter · brainstem

connectivity

edges

Outer limits or boundaries

  • graphVisual data display
  • meshInterlocking open weave
  • topologyStudy of spatial arrangement

interconnected · pathways · parallel processing · distributed · sparse · dense · bottleneck · forward pass

Categories
Opposites
computational paradigm

neural networksexpert systems

AI using encoded rules

decision trees · deductive reasoning · formal logic

knowledge representation

neural networksontologies

Frameworks of existence categories

logical inference

biological inspiration

neural networksTuring machines

Formal models of computation

Family

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