Part 6/9:
A straightforward method for training a neural network is supervised learning, where specific inputs are linked to outputs. Nunes explains that the initial choice of parameters typically yields poor results, which are then refined through iterative adjustments based on a defined cost function.
The complexity of neural networks lies not solely in their mathematical foundation but also in their sheer scale. For example, contemporary AI systems, like GPT-3.5, boast around 175 billion parameters, presenting significant challenges when it comes to optimizing their performance.