inference in neural networks
Inference in neural networks refers to the process of using a trained neural network to make predictions or draw conclusions based on input data. It involves feeding input to the network, which propagates through its layers and produces an output. The inference step utilizes the learned weights and biases in the network's connections to transform the input data into a meaningful output, such as class labels or regression predictions. Inference is the primary task performed by trained neural networks for various applications, such as image recognition, natural language processing, and recommendation systems.
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Related Concepts (20)
- active learning
- backpropagation
- bayesian inference
- bias and fairness in neural networks
- convolutional neural networks
- deep learning
- explainability in neural networks
- generative adversarial networks
- hyperparameter tuning in neural networks
- interpretability of neural networks
- multilayer perceptron
- neural network compression
- probabilistic programming
- recurrent neural networks
- reinforcement learning
- robustness of neural networks
- supervised learning
- transfer learning
- unsupervised learning
- variational inference
Similar Concepts
- artificial neural networks
- bayesian inference in causal models
- causal inference
- causal inferences
- fuzzy inference
- inference
- inference and forward propagation
- inference errors
- neural network inference
- neural network layers
- neural network modeling
- neural network models
- neural network training
- neural networks
- type inference