Deep-Learning-with-PyTorch

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 successfully. Now
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