How does TensorFlow handle model training and optimization?

TensorFlow trains and optimizes large models in Machine Learning. It is an advanced structure through which the system for building, compiling, and then further perfecting a model iteratively is achieved. While training, TensorFlow performs changes in model parameters to decrease errors by using several techniques such as gradient descent and backpropagation. Optimizers like Adam and SGD further optimize the weights of the model to achieve high accuracy. With the support of TensorFlow for GPUs and TPUs, training, especially of deep learning models on large datasets, goes much faster. Other tools, such as TensorBoard, even allow for easy visualization of training progress and tuning of hyperparameters, therefore helping users in monitoring and optimizing models for better performance.