What operations can be performed on tensors in TensorFlow?

Tensors in TensorFlow are versatile data structures that support a wide range of operations, including both reshaping and slicing. Reshaping allows users to change the dimensions of a tensor without altering its data. This flexibility is crucial when preparing data for machine learning models or adapting the tensor to the requirements of different algorithms.

Slicing enables users to extract specific subsets of data from tensors. This operation is particularly useful when manipulating large datasets, allowing for operations on smaller portions without needing to copy or recreate the entire tensor. Slicing can be done along any dimension, providing a powerful means to access and modify the data contained within a tensor.

The combination of these operations allows for efficient data manipulation in TensorFlow, making it easier for developers and machine learning engineers to preprocess data, transform inputs, and analyze outputs in various ways necessary for effective model training and evaluation.