The post provides a comprehensive guide to deep learning for computer graphics. It introduces neural networks, specifically multilayer perceptrons (MLPs), and their structure, explaining key concepts such as neurons, layers, and activation functions. The guide further covers the implementation and training of these networks, including gradient descent and backpropagation. It also touches upon advanced topics like input encodings and the Adam optimizer, and discusses common challenges in training neural networks. Recommended practices and resources for further study are provided.
Table of contents
3.1 How the Neural Network Learns3.2 Stochastic Gradient Descent3.3 When To Stop Training3.4 Cost Function3.5 Backpropagation3.6 Training Implementation Details3.7 Neural Network InitializationSort: