Statements (64)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
theoretical computer science
machine learning technique artificial intelligence method |
| gptkbp:activatedBy |
gptkb:ReLU
sigmoid tanh softmax |
| gptkbp:application |
autonomous vehicles
robotics diagnosis game playing financial prediction |
| gptkbp:component |
weights
hidden layer activation function input layer output layer |
| gptkbp:consistsOf |
artificial neurons
|
| gptkbp:developedBy |
1940s
|
| gptkbp:field |
gptkb:artificial_intelligence
gptkb:machine_learning computer science |
| https://www.w3.org/2000/01/rdf-schema#label |
Artificial Neural Networks
|
| gptkbp:inspiredBy |
biological neural networks
|
| gptkbp:limitation |
require large datasets
overfitting vanishing gradient problem require high computational power |
| gptkbp:popularizedBy |
gptkb:Geoffrey_Hinton
gptkb:Yann_LeCun gptkb:Yoshua_Bengio gptkb:Frank_Rosenblatt |
| gptkbp:relatedTo |
gptkb:perceptron
gptkb:genetic_algorithms deep learning decision trees support vector machines |
| gptkbp:standardDataset |
gptkb:CIFAR-10
gptkb:ImageNet gptkb:MNIST |
| gptkbp:trainer |
gptkb:reinforcement_learning
backpropagation gradient descent supervised learning stochastic gradient descent unsupervised learning |
| gptkbp:type |
gptkb:model
gptkb:convolutional_neural_network gptkb:feedforward_neural_network gptkb:radial_basis_function_network recurrent neural network modular neural network |
| gptkbp:usedFor |
gptkb:dictionary
control systems natural language processing speech recognition function approximation image recognition pattern recognition regression data mining forecasting |
| gptkbp:bfsParent |
gptkb:Fuzzy_Neural_Networks
|
| gptkbp:bfsLayer |
6
|