Dense layers and Convolutional layers are two commonly used layer types in deep learning models built using Keras.
A Dense layer is also known as a fully connected layer. In a Dense layer, each neuron is connected to all the neurons in the previous layer. The output of the dense layer is determined by the weights associated with each of these connections and the bias of each neuron in the layer. Dense layers are commonly used in the output layer of many models, and also in the hidden layers of models designed to process tabular data, like a spreadsheet. An example use-case of a Dense layer is image classification on tabular data, such as the Fashion-MNIST dataset.
In contrast, a Convolutional layer is designed to process image data. Convolutional layers are built around the concept of learning features from an image, and they apply a set of filters to the input image. Each filter generates a feature map highlighting the presence of a particular feature. Convolutional layers have a relatively small number of weights compared to a dense layer, and they perform the necessary convolution operation that helps some important features to be captured. Convolutional layers are organized in a hierarchical fashion, usually followed by dense layers, which learn from the output of the feature maps produced by convolution layers. Convolutional layers are commonly used in convolutional neural networks (CNNs), which often achieve state-of-the-art performance on image classification tasks.
The main difference between Dense and Convolutional layers lies in how they process the input data. While Dense layers process all dimensions of the input data, Convolutional layers take advantage of local correlations in the input. In other words, Convolutional layers look for meaningful patterns in subsections of the input image, while Dense layers consider the entire input at once. This makes Convolutional layers well-suited for image data where local patterns capture important information about the object or scene.
In conclusion, Dense layers and Convolutional layers are used for different purposes in Keras models. Dense layers are used to process tabular data, while Convolutional layers are designed for image data. Both layers can be used in combination to form powerful neural networks for both classification and regression tasks.