Dense model

Dense layers (also known as fully connected layers) are fundamental building blocks in many neural network architectures, including Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The 'dense' connectivity means that the output of each neuron in the previous layer serves as an input to every neuron in the current layer. Mathematically, the output of a dense layer can be represented as `output = activation(dot_product(weights, input) + bias)`, where `weights` is a matrix representing the connection strengths between neurons, `input` is the input vector, `bias` is a bias vector added to each neuron, and `activation` is a non-linear activation function (e.g., ReLU, sigmoid, tanh). The large number of parameters in dense layers makes them computationally expensive and prone to overfitting, especially when dealing with limited data. Techniques like dropout, weight regularization, and batch normalization are often employed to mitigate these issues. While dense layers are powerful for learning complex relationships, they may not be the most efficient choice for all types of data, such as images or text, where local patterns can be better captured by convolutional or recurrent layers, respectively. However, even in those architectures, dense layers are often used at the final stage for classification or regression tasks to aggregate the learned features.