Deep Learning and AI Superhero

1.2 Backpropagation, Gradient Descent, and Optimizers

When training a neural network, the primary objective is to minimize the loss function (alternatively referred to as the cost function). This function serves as a quantitative measure of the discrepancy between the network's predictions and the actual target values, providing a crucial metric for assessing the model's performance.

The crux of the training process lies in the intricate task of fine-tuning the model's weights and biases. This meticulous adjustment is essential for enhancing the network's predictive accuracy over time. To achieve this, neural networks employ a sophisticated learning process that hinges on two fundamental techniques: backpropagation and gradient descent.

These powerful ...