Deep Learning with PyTorch

4 Real-world data representation using tensors

This chapter covers

• Representing real-world data as PyTorch tensors
• Working with a range of data types
• Loading data from a file
• Converting data to tensors
• Shaping tensors so they can be used as inputs for neural network models

In the previous chapter, we learned that tensors are the building blocks for data in PyTorch. Neural networks take tensors as input and produce tensors as outputs. In fact, all operations within a neural network and during optimization are operations between tensors, and all parameters (for example, weights and biases) in a neural network are tensors. Having a good sense of how to perform operations on tensors and index them effectively is central to using tools like PyTorch ...