Backpropagation: adjust the ANN

The goal of backpropagation in Artificial Neural Networks (ANNs) is to train the network to make accurate predictions. It achieves this by adjusting the weights of the network based on the error between predicted and actual outputs. Backpropagation uses the gradient of the error function to update the weights layer by layer, starting from the output layer and propagating backwards. By iteratively repeating this process, the network learns to minimize the error and improve its predictive performance. Ultimately, the aim is to optimize the network's weights to make reliable predictions on new, unseen inputs.

Backpropagation, derived from "backward propagation of errors," is a technique used to train a neural network by propagating the error from the output layer back through the network. Although backpropagation is often associated with Artificial Neural Networks (ANNs), it is also applicable to other types of learning models, such as Deep Neural Networks (DNNs).

Let's explain the workings of backpropagation in simple terms for someone without a technical or mathematical background. Suppose you have a neural network that needs to be trained to recognize images of cats and dogs.

1. Forward Pass:In the first step of backpropagation, an input (such as an image) is fed through the network. It passes through various layers of neurons, with each layer having a set of weights and activation functions. These weights determine how the input is processed and interpreted.
2. Making a Prediction:During the forward pass, the network calculates a prediction, such as whether the image is a cat or a dog. This prediction is compared to the actual label information to determine the error.
3. Backward Pass:Now, the actual backpropagation process begins. The goal is to propagate the error of the prediction back through the network and adjust the weights of each layer.
4. Gradient Descent:Backpropagation uses an optimization algorithm called gradient descent to adjust the weights. Gradient descent calculates the direction and amount by which the weights should be adjusted to reduce the error. The idea is to update the weights incrementally in the direction that leads to better prediction.
5. Iterations and Repetition:The process of forward pass, error calculation, and backward pass is repeated over multiple iterations. This ensures that the network gradually learns and optimizes the weights to make better predictions.
   

Backpropagation is effective because it adjusts the network weights based on the error found in the predictions. This allows the network to learn from its mistakes and improve the accuracy of the predictions.
While backpropagation is primarily used in neural networks, the concept of error propagation and weight adjustment can also apply to other models that rely on optimizing parameters to minimize a loss function.