Training and Evaluation

Embarking on the journey of training and evaluating neural networks is a pivotal phase in the deep learning workflow. This chapter aims to equip you with the knowledge and skills required to effectively train and assess your models using PyTorch. You'll learn the intricacies of configuring the training loop, optimizing model parameters, and implementing evaluation metrics to gauge performance.

We will explore techniques for splitting datasets into training and validation subsets, understanding the backpropagation algorithm, and leveraging optimizers to minimize loss functions. You'll gain hands-on experience in adjusting hyperparameters and utilizing PyTorch's autograd feature to automatically compute gradients. Additionally, we'll cover the significance of model evaluation using metrics such as accuracy, precision, and recall, ensuring you can interpret and enhance model outcomes.

By the end of this chapter, you will be proficient in setting up and executing training routines, enhancing your ability to build powerful and efficient neural networks.