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The TensorFlow Workshop
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Tensors can be thought of as the core components of ANNs—the input data, output predictions, and weights that are learned throughout the training process are all tensors. Information propagates through a series of linear and nonlinear transformations to turn the input data into predictions. This section demonstrates how to apply linear transformations such as additions, transpositions, and multiplications to tensors. Other linear transformations, such as rotations, reflections, and shears, also exist. However, their applications as they pertain to ANNs are less common.
Tensors can be represented as multi-dimensional arrays. The number of dimensions a tensor spans is known as the tensor's rank. Tensors with ranks 0
, 1
, and 2
are used often and have their own names, which are scalars, vectors, and matrices, respectively, although the term tensors can be used to describe each of them. Figure 1.2 shows...