In this topic, we will introduce linear transformations. Linear transformations are the backbone of modeling with ANNs. In fact, all the processes of ANN modeling can be thought of as a series of linear transformations. The working components of linear transformations are scalars, vectors, matrices, and tensors. Operations such as additions, transpositions, and multiplications are performed on these components.
Scalars, vectors, matrices, and tensors are the actual components of any deep learning model. While they may be simple in principle, having a fundamental understanding of how to utilize all types, as well as the operations that can be performed on them. It is key to the mathematics of ANNs. Scalars, vectors, and matrices are examples of the general entity known as a tensor, so the term tensors may be used throughout this chapter but may refer to any component. Scalars, vectors, and matrices refer to tensors with a specific...
