Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide

Deep learning becomes easier to follow when the ideas behind the code are made visible. Daniel Voigt Godoy’s Deep Learning with PyTorch Step-by-Step starts with fundamentals such as gradient descent and simple regression, then builds toward computer vision, sequence modeling, and natural language processing. Its first-principles approach is designed to help readers understand not just how to construct a model, but what is happening as it learns.

Start with the mechanics of learning

The book opens by working through gradient descent and basic regression and classification problems. Along the way, it introduces the training loop, gradients, loss, optimizers, autograd, and PyTorch’s dynamic computation graph. This gradual groundwork gives readers a way to connect familiar machine-learning ideas to the framework’s practical tools.

From image models to Transformers

Later sections expand into computer vision, including convolutions, activation functions, data augmentation, and transfer learning. The focus then shifts to sequential data and language: recurrent neural networks, GRUs, LSTMs, sequence-to-sequence models, attention, self-attention, and Transformers. The natural language processing material also addresses tokenization, embeddings, BERT, and GPT-2.

Code explained alongside the concepts

Rather than treating implementation as a black box, the book develops models step by step and discusses the reasoning behind the code. That makes the progression useful for readers who want to build a clearer mental model of how PyTorch handles training and model construction—not simply collect snippets to copy.

Who may find it useful?

This guide is intended for beginners in deep learning and PyTorch. Readers with some background in machine learning can also use its explanations of core concepts as a bridge from theory to practical model-building. The first-edition version 1.2 revision updates material for PyTorch 2.x.

Build understanding one model at a time

From a first gradient update to modern language-model techniques, Godoy’s structured progression keeps the emphasis on understanding how the pieces fit together. It offers a substantial route through the foundations and major applications of deep learning with PyTorch.