PyTorch The Practical Guide

In this book, you'll learn about:

1. Theory

   Get a thorough grounding in the concepts behind your models. Whether you’re looking to understand how a confusion matrix or ROC curve helps you evaluate a classification model or you want to grasp how recommendation system algorithms function, this guide has got you covered.

2. Practice

   Move beyond theory with hands-on exercises and code. Create datasets for your linear regression models, use diffusion to create realistic images from noise, process sequential data with recurrent neural networks, and more.

3. Deployment and Evaluation

   Monitor your training process, visualize metrics, and evaluate models with tools like MLflow and TensorBoard. Deploy models on-premise with FastAPI or in the cloud with Heroku.