FFNN (Feed Forward Neural Network)

Information

• A FFNN (Feed-Forward Neural Network) is a type of artificial neural network in which the information flows only in one direction, from input layer to output layer.
• The network consists of one or more hidden layers, each of which contains a number of neurons or processing units that transform the input signals using a nonlinear activation function, such as the sigmoid, ReLU, or tanh function.
• The output of each neuron in a hidden layer is a weighted sum of its inputs, plus a bias term, which is then passed through the activation function to produce the output of the neuron.
• The weights and biases of the neurons are learned through a process called backpropagation, which involves computing the gradient of the loss function with respect to the network parameters and updating them using an optimization algorithm, such as stochastic gradient descent (SGD) or Adam.
• FFNNs can be used for various tasks, such as classification, regression, or function approximation, and can model complex input-output relationships that are nonlinear, non-monotonic, or high-dimensional.
• FFNNs can also be extended to include other types of layers, such as convolutional layers for image processing, recurrent layers for sequential data, or attention layers for attention-based models.
• FFNNs can be trained on large datasets using parallel computing and distributed systems, such as GPUs, TPUs, or clusters, and can be regularized using techniques such as dropout, weight decay, or early stopping to prevent overfitting and improve generalization.
• FFNNs have been successfully applied to various domains, such as speech recognition, natural language processing, computer vision, and financial prediction, and have achieved state-of-the-art performance in many benchmark datasets and competitions.