At this level, individuals are expected to have a basic understanding of PyTorch and its core concepts. They should be familiar with tensors, their operations, and the concept of automatic differentiation (Autograd). They should also understand the basics of neural networks and how they can be trained in PyTorch.

Novices should be able to create and manipulate tensors, implement Autograd, and build simple neural networks using PyTorch. They should also be capable of training these networks with basic data and debugging simple issues in their PyTorch code.

Intermediate users should be proficient in implementing complex tensor operations and using Autograd for complex computations. They should be able to design and implement more complex neural networks, train these networks with real-world data, and optimize their PyTorch code for better performance. Understanding and implementing convolutional neural networks (CNN) is also expected at this level.

Advanced users should have a deep understanding of PyTorch's internals and be capable of implementing custom layers and operations. They should be able to design and implement state-of-the-art neural networks, debug and optimize their PyTorch code at an advanced level, and implement and train complex models like GANs and RNNs. Knowledge of transfer learning is also expected.

Experts are expected to contribute to the PyTorch codebase, design and implement novel neural network architectures, and perform advanced performance tuning of PyTorch code. They should be capable of leading large-scale projects using PyTorch, publishing research based on their work in PyTorch, and teaching and mentoring others in PyTorch.