Final answer:
Forward propagation is a process in supervised learning where inputs pass through a Feedforward Neural Network (FFN) to produce an output. This output is used for training by comparing it to the actual target, with errors informing subsequent weight adjustments. The FFN learns to make accurate predictions through iterations of this process.
Step-by-step explanation:
Understanding Forward Propagation in Supervised Learning on Feedforward Neural Networks
Forward propagation is a term used in the context of neural networks, specifically Feedforward Neural Networks (FFNs), within the field of supervised learning. This process refers to the movement of inputs through the network to generate an output. During forward propagation, the inputs are processed through successive layers of the network, with each layer applying a set of weights to the inputs, adding a bias term, and usually passing the resulting sum through a nonlinear activation function. The output of one layer becomes the input to the next layer until the final layer produces the network's output.
The primary goal of forward propagation in supervised learning is to make predictions or decisions based on the input data. These predictions are then compared to the actual target outputs during the training phase, and the error is used to adjust the weights and biases through a process called backpropagation. Over time, through multiple iterations of forward and backward propagation, the neural network 'learns' to make more accurate predictions.
Forward propagation is a critical step in training a neural network, as it sets the stage for error evaluation and weight adjustments. By repeatedly applying forward propagation followed by backpropagation, the supervised learning system optimizes the model to reduce prediction errors on the training data. Once trained, the FFN can be used on new, unseen data to make predictions in accordance with its learned parameters.