A practical guide to building and training deep learning models using PyTorch. The book introduces PyTorch tensors, neural networks, data loading, model training, loss functions, monitoring, and visualization through hands-on projects. It focuses on applying deep learning to real-world problems, including image classification and medical imaging, while teaching techniques such as data augmentation, model improvement, and fine-tuning.