With a firm grasp of PyTorch fundamentals and how they relate to your TensorFlow experience, you are now prepared to examine some of PyTorch's more advanced capabilities. This chapter focuses on functionalities that offer finer control over your models, improve training performance, and assist in troubleshooting.

You will learn about:

• PyTorch Hooks: How to use them to inspect or alter gradients and activations during the forward and backward passes.
• Distributed Training: An overview of scaling your training across multiple GPUs or machines, drawing comparisons to tf.distribute.Strategy.
• Mixed Precision Training (AMP): Utilizing PyTorch's automatic mixed precision to accelerate training and reduce memory consumption.
• Profiling: Using PyTorch's built-in profiler to identify performance bottlenecks in your models and data pipelines.
• The PyTorch Ecosystem: A brief introduction to domain-specific libraries such as torchvision, torchaudio, and torchtext.
• Debugging Techniques: Practical strategies for identifying and resolving common issues encountered when working with PyTorch models.

These tools and techniques will help you build more sophisticated and efficient machine learning solutions in PyTorch, enhancing your ability to transition complex workflows.