90% of PyTorch Users Struggle with Generative Models: Here's Why

Understanding generative models in PyTorch can be challenging, with 90% of users facing difficulties due to the complexity of these models. This article delves into two critical concepts for mastering generative models: backpropagation of error and batch processing. We start with a basic bigram model, similar to those in Andrej Karpathy’s makemore series, which is trained one example at a time. Then, we introduce the DataLoader class in PyTorch, which handles batch processing and padding, essential for efficient training. This tutorial avoids using pre-built neural network models, focusing instead on the underlying mechanics. By understanding these fundamentals, users can better grasp more complex models like Transformers or LSTMs, enhancing their ability to work with generative models effectively.

Source: towardsdatascience.com