AI and ML for Coders in PyTorch

A coder-first path into PyTorch and modern AI

AI and ML for Coders in PyTorch is built for developers who want to work with machine learning in a practical, code-first way. Laurence Moroney keeps the focus on doing: building models, testing ideas, and learning the core concepts behind AI without turning the book into a math hurdle.

The journey begins with the basics of machine learning and PyTorch, then expands into computer vision, dataset handling, natural language processing, embeddings, recurrent neural networks, and sequence modeling. From there, the book moves into the newer territory readers are actively looking for today: pretrained models, retrieval-augmented generation, Hugging Face Diffusers, and LoRA fine-tuning.

What the book covers

• Getting started with PyTorch and machine learning fundamentals
• Computer vision, convolutions, and image classification
• Practical dataset workflows and data loading
• Natural language processing and embeddings
• RNNs, time-series data, and sequence prediction
• Generative AI workflows, RAG, and model tuning

Built for developers who learn by building

This is a strong fit for programmers, technical students, and self-directed learners who want a structured introduction to AI that stays close to real implementation. The book’s progression from classic ML tasks to generative AI makes it useful both as a learning guide and as a reference for hands-on experimentation.

A broad, modern survey of applied AI

What stands out here is the range. Instead of stopping at introductory deep learning, the book continues into the tools and methods shaping current AI work. That makes it especially relevant for readers who want one volume that covers both the foundations and the newer PyTorch-based workflows surrounding generative models.

If you’re looking for a practical AI ebook with a clear developer focus, this title belongs on the shortlist.