PyTorch for AI Applications

Want to move beyond AI theory and build practical deep learning applications with Python and PyTorch? This hands-on guide takes you from tensor operations and neural network training to model optimization, inference, and deployment.

Building an AI application involves more than defining a neural network and running a training script. You need to prepare data, choose suitable model architectures, evaluate results, diagnose errors, and ensure that your trained model performs reliably when used in a real application. PyTorch for AI Applications guides you through these essential stages with practical explanations and code examples designed to help you understand not only how things work, but why they matter.

Starting with PyTorch fundamentals, you will learn to work with tensors, automatic differentiation, neural network layers, training loops, loss functions, and optimizers. You will then explore practical applications involving computer vision, natural language processing, structured data, and recommendation systems. The book also covers model evaluation, performance optimization, checkpoint management, inference, and exporting models for compatible deployment workflows.

Finally, you will bring these skills together in a complete AI application project, taking a customer-session purchase classifier from data preparation and training through evaluation, optimization, and API deployment with FastAPI. Troubleshooting guidance, an essential PyTorch API reference, environment setup advice, and further learning resources provide additional support as you develop your own projects.

• Learn by building: Apply PyTorch concepts through practical examples and end-to-end development workflows.

• Build and train neural networks: Understand tensors, automatic differentiation, model architectures, loss functions, optimizers, and training strategies.

• Explore real AI applications: Work with computer vision, natural language processing, tabular prediction, and recommendation systems.

• Improve model performance: Learn to evaluate models, investigate errors, manage checkpoints, optimize inference, and measure performance.

• Prepare models for deployment: Explore model export, inference workflows, and building a prediction API with FastAPI.

Tad E. Preston is the author of PyTorch for AI Applications, a practical guide to learning deep learning development with Python and PyTorch. His approach emphasizes clear explanations, hands-on implementation, and connecting model development with the process of building usable AI applications.

Whether you are a Python developer expanding into AI or an aspiring machine learning practitioner ready to build beyond basic examples, this book offers a practical path from PyTorch fundamentals to a deployed application.

Get your copy of PyTorch for AI Applications and start building, training, evaluating, and deploying your own deep learning projects.