Building upon the foundational tensor operations covered previously, this chapter introduces techniques for more advanced tensor manipulation. Gaining fine-grained control over tensor structure, data types, and device placement is fundamental for preparing data and implementing complex deep learning models effectively.

In this chapter, you will learn to:

• Select and modify tensor elements precisely using indexing and slicing methods.
• Restructure tensors without changing their data using view(), reshape(), and permute().
• Combine multiple tensors or divide a single tensor into parts with cat(), stack(), split(), and chunk().
• Apply operations between tensors of compatible but different shapes using PyTorch's broadcasting mechanism.
• Work with different numerical data types (e.g., float, int) and cast tensors accordingly.
• Transfer tensors between CPU memory and GPU accelerators using .to(device).

Mastering these manipulations is necessary for handling the diverse data formats and computational requirements encountered in deep learning workflows.