The most magical part of deep learning, in my opinion, is the ability of a neural network to ‘learn’ from data. But this isn’t magic; it’s a beautifully logical mathematical process. The network starts by making random guesses and then gradually improves by measuring its mistakes and adjusting its internal parameters to become more accurate over time.
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This learning process is driven by two key components: a loss function, which quantifies how wrong the network’s predictions are, and an optimizer, which guides the network on how to adjust its parameters to reduce that error. This guide will explain how these two pieces work together to enable learning.
📉 The Loss Function: Measuring the Error
The first step in learning is to have a way to measure the network’s performance. This is the job of the loss function (also known as a cost function or objective function). It takes the network’s predictions and the true target values and calculates a single number—the loss—that represents how far off the predictions are.
A common loss function for regression problems (predicting a numerical value) is the Mean Squared Error (MSE). For each prediction, it calculates the square of the difference between the predicted value and the actual value. The goal of the learning process is to find the set of weights and biases for the network that makes the value of this loss function as small as possible.
⚙️ The Optimizer: Minimizing the Loss
Once I have a way to measure the error, I need a strategy to minimize it. This is the role of the optimizer. The most common optimization algorithm is called Gradient Descent. I like to imagine the loss function as a hilly landscape, where the goal is to find the lowest point, or the ‘global minimum’.
Gradient Descent does this by taking small steps in the direction of the steepest descent—the direction that will reduce the loss the most. This ‘direction’ is determined by calculating the gradient, which is a mathematical concept that points downhill. The size of each step is controlled by a parameter called the learning rate.
The process is iterative:
- The network makes a prediction.
- The loss function calculates the error.
- The optimizer calculates the gradient of the loss with respect to the network’s weights.
- The weights are updated slightly in the opposite direction of the gradient.
This process is repeated thousands or even millions of times, and with each step, the network gets a little better at its task, gradually descending into a valley of low error.
- A Practical Guide to Overfitting and Regularization in Deep Learning
- A Guide to Generative Adversarial Networks (GANs)
- A Guide to Autoencoders for Dimensionality Reduction
- A Guide to Long Short-Term Memory (LSTM) Networks
- A Guide to Recurrent Neural Networks (RNNs) for Sequential Data
- A Guide to Convolutional Neural Networks (CNNs) for Image Recognition
- A Guide to Backpropagation – How Neural Networks *Really* Learn
