In this topic, we’ll explore the fascinating process of how neural networks learn to adjust, correct, and improve over time. We’ll start with Hebbian learning—the biological idea that "neurons that fire together, wire together." This concept shows how the strength of connections between neurons is shaped by their correlation in unsupervised settings, helping the network learn from patterns in data.

Next, we’ll introduce the Delta Rule, which brings in error-based learning—this is the key to how neural networks figure out how to fix their mistakes. From there, we’ll lay the groundwork for gradient descent, the powerful method that guides the network toward better solutions by minimizing errors step by step.

Finally, we’ll dive into the world of gradient-based optimization, where the process of learning is like a journey through an "error landscape." Along the way, you'll learn how to navigate key challenges like understanding learning rates, avoiding local minima, and overcoming saddle points—all essential to mastering how a neural network converges to its optimal solution.

• How does Hebbian learning illustrate the idea of association without explicit supervision?

• In what ways does the Delta Rule transform the idea of “learning from mistakes” into mathematics?

• How can visualizing the loss landscape help us understand why neural networks sometimes learn too slowly—or not at all?