PyTorch: The Practical Guide

PyTorch is the framework for deep learning—so dive on in! Learn how to train, optimize, and deploy AI models with PyTorch by following practical exercises and example code. You’ll walk through using PyTorch for linear regression, classification, image processing, recommendation systems, autoencoders, graph neural networks, time series predictions, and language models—all the essentials. Then evaluate and deploy your models using key tools like MLflow, TensorBoard, and FastAPI. With information on fine-tuning your models using HuggingFace and reducing training time with PyTorch Lightning, this practical guide is the one you need!

• Train, tune, and deploy deep learning models with PyTorch
• Implement models for linear regression, classification, computer vision, recommendation systems, and more
• Work with PyTorch Lightning, TensorBoard, LangChain, and FastAPI

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.

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.

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.

Bert Gollnick is a senior data scientist who specializes in renewable energies. For several years, he has taught courses about data science and machine learning, and more recently, about generative AI and natural language processing. Bert studied aeronautics at the Technical University of Berlin and economics at the University of Hagen. His main areas of interest are machine learning and data science.