TensorFlow Variables: Essential Guide

Variables in TensorFlow are parameters that need to be optimized during the model training process, can be persisted, and remain unchanged across multiple calls. In TensorFlow, variables are used to store and update parameters such as weights and biases in a neural network. By using variables, one can continuously optimize the model parameters during training, enabling the model to learn and improve performance consistently. TensorFlow variables can be created using the tf.Variable() function and their values can be updated using methods like assign().