neural network layers
Neural network layers are individual components within a neural network that consist of interconnected nodes, also known as neurons, organized in a specific pattern. They serve as information processing units, where each neuron receives input data, performs computations based on weighted connections, and produces an output that is passed onto subsequent layers. These layers help to transform and extract features from input data, enabling the network to learn and make predictions or classifications.
Requires login.
Related Concepts (15)
- attention layers
- autoencoder layers
- batch normalization layers
- convolutional layers
- dropout layers
- embedding layers
- fully connected layers
- gated recurrent unit (gru) layers
- generative adversarial network (gan) layers
- long short-term memory (lstm) layers
- pooling layers
- recurrent layers
- residual layers
- softmax layers
- transformer layers
Similar Concepts
- artificial neural networks
- convolutional neural networks
- inference in neural networks
- multilayer perceptron
- multilayer perceptrons
- neural circuits
- neural network architecture
- neural network architectures
- neural network control
- neural network inference
- neural network modeling
- neural network models
- neural network training
- neural networks
- recurrent neural networks