Dense layers and
convolutional layers are two fundamental types of layers used in neural networks, each serving different purposes and operating on different types of data. Here's a brief comparison between Dense and Convolutional layers:
Dense Layer :
* Also known as a fully connected layer.
* Each neuron in a dense layer is connected to every neuron in the previous layer.
* It operates on flattened input data, meaning that it takes a one-dimensional array of input features.
* Dense layers are typically used in the early stages of a neural network to process feature vectors or flattened representations of input data.
* They are commonly used for tasks such as image classification, text classification, and regression.
Convolutional Layer :
* Convolutional layers apply convolution operations to input data.
* They operate on multi-dimensional input data such as images or sequences (e.g., time series data).
* Convolutional layers use learnable filters or kernels that slide across the input data and perform element-wise multiplications and summations.
* Convolutional layers are designed to capture spatial patterns and local dependencies in the input data, making them well-suited for tasks involving spatial relationships, such as image processing and computer vision.
* They are commonly used to extract features from input data hierarchically, capturing low-level features (e.g., edges and textures) in early layers and high-level features (e.g., shapes and objects) in later layers.
