4 b] Discuss the working of backpropagation.
Backpropagation is a key algorithm used to train artificial neural networks, enabling them to learn from data by adjusting the weights of the network’s connections to minimize errors. Here’s a step-by-step breakdown of how backpropagation works:
1. Forward Pass
- The process begins by feeding the input data into the neural network.
- Each neuron in the network processes the input using a specific activation function and passes the result forward to the next layer.
- This continues through all layers until the final output layer is reached, where the network produces a prediction or output.
2. Calculate the Error
- Once the network has generated its output, it compares the predicted value with the true value (ground truth) from the dataset.
- The error or difference between the predicted output and the true output is calculated. This error measures how far the prediction is from the expected result.
3. Backward Pass (Backpropagation)
- Backpropagation begins by taking this error and propagating it backward through the network, starting from the output layer and working back to the input layer.
- In each layer, the error is distributed to each neuron according to its contribution to the overall error.
- Essentially, backpropagation adjusts the weights in the network by computing how much each weight contributed to the error, without explicitly calculating the gradients (the slope of the error with respect to the weights). The goal is to reduce the error by adjusting the weights incrementally.
4. Gradient Descent Optimization
- Once the error is propagated back, the network adjusts the weights by a small amount, in a direction that reduces the error. This is done by calculating the gradients of the error with respect to the weights and making updates accordingly.
- The learning rate determines the size of the weight adjustments. If the learning rate is too large, the model may overshoot the optimal values, and if it is too small, the learning process may be slow.
5. Repeat the Process
- This forward-backward process is repeated for many iterations, each time adjusting the weights to reduce the overall error.
- As the network trains, the weights are refined to make better predictions over time, minimizing the difference between the predicted and true values.