Writing Neural Networks with PyTorch
Summary This post provides a practical guide to building common neural network architectures using PyTorch. We’ll explore feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTMs, transformers, autoencoders, and GANs, along with code examples and explanations.
1. Understanding PyTorch’s Neural Network Module PyTorch provides the torch.nn module to build neural networks. It provides classes for defining layers, activation functions, and loss functions, making it easy to create and manage complex network architectures in a structured way.