Knowledge Graph — Coursera Notes › Academic disciplines › Computer Science / Information Technology › Artificial Intelligence › Deep Learning
Dense
feature · part of Deep Learning
A Dense layer, also known as a fully connected layer, is a fundamental building block in neural networks where every neuron in the layer is connected to every neuron in the previous layer. It performs a linear transformation on the input data, followed by an activation function (e.g., ReLU or Softmax). The layer has a specified number of neurons, each with its own weight and bias parameters that are learned during training. Dense layers are used in various deep learning architectures, such as multilayer perceptrons (MLPs), convolutional neural networks (CNNs) for classification, and as the final output layer in many models. As a feature of Deep Learning, Dense layers enable the network to learn complex patterns by combining inputs in a weighted manner. They are often paired with activation functions like ReLU (to introduce non-linearity) or Softmax (for multi-class classification output).
Connections
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