Gradient descent, how neural networks learn | Chapter 2, Deep learning

In this video, Grant Sanderson recaps the structure of a neural network and introduces gradient descent, explaining how neural networks learn and how machine learning works. He uses the example of handwritten digit recognition to illustrate the concepts, discussing how the network adjusts weights and biases based on training data from the MNIST database. Sanderson explains the cost function, how it measures the network's performance, and how gradient descent helps minimize this function by adjusting weights and biases. He also touches upon the limitations of the network, such as its inability to handle random images or draw digits, and briefly discusses more advanced techniques and resources for further learning, including an interview snippet with Lisha Li about recent papers on image recognition networks.