Gradient descent, how neural networks learn: Deep learning - Part 2 of 4

The video recaps neural network structure and introduces gradient descent, a key concept in machine learning. It explains how networks learn through adjusting weights and biases to minimize a cost function. The video discusses the performance of a neural network on handwritten digit recognition and explores its limitations. It encourages active engagement with the material and provides resources for further learning. The video concludes with insights into modern image recognition networks and their learning processes.

What new insight or understanding did you gain from this video?