How Neural Networks Learn

Training a neural network involves repeatedly making predictions, measuring errors, and adjusting parameters to improve performance.

1. Forward Propagation

During forward propagation, input data moves through the network layer by layer. Each neuron calculates an output, and the final layer produces a prediction.

2. Loss Function

A loss function measures how far the model's predictions are from the expected results.

Common examples include:

• Mean Squared Error (MSE): Frequently used for regression.
• Binary Cross-Entropy: Commonly used for binary classification.
• Categorical Cross-Entropy: Commonly used for multiclass classification.

The objective is to minimize the loss during training.

3. Backpropagation

Backpropagation calculates how the loss changes with respect to the network's weights and biases. It uses these gradients to determine how the parameters should be adjusted.

4. Optimization

An optimizer updates the model's parameters using the calculated gradients.

Common optimizers include:

• Gradient Descent: Updates parameters in the direction that reduces the loss.
• Stochastic Gradient Descent (SGD): Updates parameters using individual examples or small batches.
• Adam: Adapts the learning rate for different parameters and is widely used in deep learning.

The learning rate controls the size of parameter updates. A rate that is too high can make training unstable, while one that is too low can make training slow.

Epochs and Batches

An epoch is one complete pass through the training dataset. A batch is a subset of the data processed before a parameter update.

Training typically involves multiple epochs, with the model learning gradually as it processes the data.