3-1 tensor

In the context of deep learning, tensors refer to the generalization of vectors and matrices to an arbitrary number of dimensions, as we can see in figure 3.2. Another name for the same concept is multidimensional array. The dimensionality of a tensor coincides with the number of indexes used to refer to scalar values within the tensor.

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Compared to NumPy arrays, PyTorch tensors have a few superpowers, such as the ability to perform very fast operations on graphical processing units (GPUs), distribute operations on multiple devices or machines, and keep track of the graph of computations that created them.

1.The world as floating-point numbers
Since floating-point numbers are the way a network deals with information, we need a way to encode real-world data of the kind we want to process into something digestible by a network and then decode the output back to something we can understand and use for our purpose.

A deep neural network typically learns the transformation from one form of data to another in stages, which means the partially transformed data between each stage can be thought of as a sequence of intermediate representations.In general, such intermediate representations are collections of floating-point numbers that characterize the input and capture the data’s structure in a way that is instrumental for describing how inputs are mapped to the outputs of the neural network.

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