Part 7/9:
The concept of gradient descent plays a vital role in the training process of neural networks. By following the gradient—where the function is steepest—the adjustments made to parameters move the network closer to an optimal solution. This iterative approach works towards minimizing the cost function associated with incorrect predictions.
While supervised learning is widely used, other methods exist, such as reinforcement learning, where the AI learns through rewards and penalties instead of predefined input-output pairs. Each method has inherent advantages and potential pitfalls, especially concerning the design of reward functions—essentially shaping the AI’s behavior.