Fully connected neural network
A fully connected neural network is a feedforward neural architecture in which each unit in a layer is connected to every unit in the adjacent layer.
Deep Learning with R, Third Edition
A dense neural network is a neural network architecture in which units in one layer are connected to all units in the adjacent layer.
- How it is used
- Processes vector or structured data and commonly forms final classification or regression stages.
- How deep it goes
- Defines all-to-all adjacent-layer connections, contrasts local convolution, and demonstrates practical classification and regression configurations.
- Examples
- Uses California housing, Jena temperature forecasting, and compact Keras models for binary, categorical, multilabel, and regression tasks.
- What you can do after
- Build it from scratch in R/Keras by selecting Dense layers, output activations, losses, optimizers, metrics, and evaluation procedures.
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Grokking Machine Learning
A neural-network layer in which each unit receives input from all units in the preceding layer.
- How it is used
- Classifies handwritten digits and a circular two-class dataset with small neural networks.
- How deep it goes
- Demonstrates network architectures, training, and testing evaluation, then notes where flattened-image inputs fall short.
- Examples
- Uses MNIST digit images, a circular binary dataset, and a 3 misclassified as an 8.
- What you can do after
- Apply TensorFlow Keras to train a small classifier and evaluate its predictions on test data.
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Related concepts
- N-BEATSused by · N-BEATS is a neural forecasting model organized as stacks of blocks for modeling time series without requiring explicit series decomposition or input transformations.
- deep learningkind of · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
- linear layerhas part · A linear layer is a neural-network layer that maps input features to outputs by a learned affine transformation, typically connecting each output to every input feature.
- recurrent neural networkcontrasts with · A recurrent neural network is a neural network that processes sequences by updating an internal state across successive inputs, allowing earlier inputs to influence later computations.
- feedforward networkequivalent to · A feedforward network is a neural network or subnetwork in which information flows from inputs toward outputs through successive computations without recurrent connections or cycles.
- sequencecontrasts with · A sequence is an ordered collection of elements whose positions determine their order of traversal or processing.
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