Feedforward neural networks (FFN) are a type of artificial neural network (ANN) that is used in machine learning. FFNs are the simplest type of ANNs, consisting of layers of neurons connected in a directed acyclic graph (DAG). Each layer of neurons is connected to the next layer and the output of the previous layer serves as the input to the next layer. FFNs are used in supervised learning tasks, such as classification and regression.
FFNs are composed of an input layer, one or more hidden layers, and an output layer. The input layer receives the input data and passes it to the hidden layers, which process the data and pass the output to the output layer. The output layer produces the desired output from the input data. Each layer of neurons is connected to the next layer in a feedforward manner, meaning that the output of one layer is used as the input for the next layer.
FFNs use a variety of activation functions to determine the output of each neuron. Common activation functions include the sigmoid, hyperbolic tangent, and rectified linear unit (ReLU). The activation function determines the output of the neuron based on the input data and the weights associated with the neuron.
FFNs are trained using a process called backpropagation. The weights and biases of the network are adjusted based on the error between the desired output and the actual output. This process is repeated until the desired output is achieved.
FFNs are used in a variety of tasks, including image recognition, natural language processing, and robotics. They are also used in applications such as fraud detection and autonomous vehicles. FFNs are a powerful tool for machine learning, and they are widely used in many different industries.