Deep-Learning-with-PyTorch

It starts with a tensor
This chapter covers
• Understanding tensors, the basic data structure
in PyTorch
• Indexing and operating on tensors
• Interoperating with NumPy multidimensional
arrays
• Moving computations to the GPU for speed
In the previous chapter, we took a tour of some of the many applications that deep
learning enables. They invariably consisted of taking data in some form, like images
or text, and producing data in another form, like labels, numbers, or more images
or text. Viewed from this angle, deep learning really consists of building a system
that can transform data from one representation to another. This transformation is
driven by extracting commonalities from a series of examples that demonstrate the
desired mapping. For example, the system might note the general shape of a dog
and the typical colors of a golden retriever. By combining the two image properties,
the system can correctly map images with a given shape and color to the golden
retriever label, instead of a black lab (or a tawny tomcat, for that matter). The
resulting system can consume broad swaths of similar inputs and produce meaningful
output for those inputs.
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