Neural Networks and Deep Learning: How Modern Models Learn

The training process involves adjusting the network weights through backpropagation and optimization algorithms. Important aspects include the choice of architecture, data volume, regularization, and computing resources. Modern practice includes transfer learning, when the network is pre-trained on one task and fine-tuned for another, which saves time and resources.