Tensorbewerking

Tensorbewerking refers to the manipulation and transformation of tensors, which are multi-dimensional arrays of numbers. Tensors are a fundamental concept in various fields, including physics, engineering, machine learning, and computer graphics. In essence, a tensor is a generalization of scalars (0-dimensional tensors), vectors (1-dimensional tensors), and matrices (2-dimensional tensors) to higher dimensions.

The operations performed on tensors can vary widely. Common tensor operations include addition, subtraction, scalar multiplication, and element-wise multiplication (Hadamard product). More complex operations involve tensor contraction, which is a generalization of matrix multiplication. Tensor contraction effectively sums over pairs of indices, reducing the dimensionality of the resulting tensor. Transposition, reshaping, and slicing are also common tensor manipulation techniques used to reorganize or extract specific parts of a tensor.

In machine learning, tensor operations are the backbone of neural networks. Libraries like TensorFlow and PyTorch are specifically designed for efficient tensor computation, enabling the training of complex models. These libraries leverage hardware accelerators like GPUs and TPUs to perform these operations at high speeds. Understanding tensorbewerking is crucial for anyone working with modern data science and artificial intelligence.