How Does Backpropagation Work?
Backpropagation is a fundamental algorithm used in training neural networks, particularly in the context of deep learning for natural language processing (NLP). The goal of backpropagation is to minimize the difference between the predicted output and the actual target through optimization of the network's weights.
1. Forward Pass
During the forward pass, input data is fed through the neural network layer by layer. Each neuron processes the input by applying weights and a non-linear activation function, producing an output that contributes to the final prediction.
2. Loss Calculation
After the forward pass, the loss function evaluates the prediction’s accuracy by comparing the predicted outputs with the actual targets. This generates a scalar value representing how far the model's predictions are from the true labels.
3. Backward Pass
In the backward pass, backpropagation computes the gradient of the loss function concerning each weight in the network. Using the chain rule of calculus, the algorithm propagates the error back through the network, effectively distributing the loss across the layers.
4. Weight Updates
Finally, the computed gradients are used to update the weights of the network using an optimization algorithm like Stochastic Gradient Descent (SGD) or Adam. This adjustment aims to reduce the loss in subsequent iterations.
Through this iterative process of forward and backward passes, backpropagation enables the neural network to learn and improve its predictions over time, making it an essential technique in deep learning applications, particularly in NLP.