PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models, Second Edition

A Practical Recipe Book for PyTorch Developers

PyTorch Recipes, Second Edition is built for readers who want to work through real PyTorch tasks rather than read abstract theory for hundreds of pages. Pradeepta Mishra organises the material as a series of problems and solutions, moving from tensors and probability distributions to CNNs, RNNs, supervised learning, and fine-tuning. The result is a reference you can keep close while experimenting, debugging, or expanding a model.

What the Book Covers 💻

The chapters follow a clear progression through the PyTorch ecosystem:

• Tensors and tensor operations – the foundation of every PyTorch workflow
• Probability distributions – using PyTorch to work with distributions and gradients
• CNNs and RNNs – loss functions, optimisation, convolution, recurrence, autoencoders, dropout, and embedding layers
• Neural networks – activation functions, network construction, and tensor differentiation
• Supervised learning – data preparation, forward and backward propagation, logistic regression, and predictions
• Fine-tuning – sequential networks, batch size, learning rate, and parallel training

Learn by Solving Specific Problems ⚙️

Each recipe is designed to be practical. Instead of presenting a single long tutorial, the book isolates individual tasks: setting up a loss function, estimating a derivative, selecting an optimiser, implementing a CNN or RNN, visualising overfitting, initialising weights, or choosing a batch size. That structure makes it easier to jump to the technique you need and adapt it to your own project.

From Concepts to Working Models 🧠

The book connects core ideas to executable PyTorch code. Readers work with activation functions, gradient computation, autoencoders, regression and classification models, and fine-tuning strategies. The later recipes focus on model behaviour and training decisions, helping you understand why certain settings affect performance and how to adjust them.

Who This Book Is For

PyTorch Recipes, Second Edition suits developers, data scientists, and students who already know some Python and machine learning and want a structured, hands-on PyTorch reference. If you learn best by implementing, testing, and adjusting code, the problem-solution format will feel natural. Beginners may want a foundational Python and ML background before starting, while experienced practitioners can use the recipes as a quick lookup for common deep learning tasks.

A Focused Reference for Building, Training, and Deploying Models

The second edition keeps its attention on the workflow that matters in practice: prepare data, define a model, compute gradients, choose an optimiser, train, evaluate, and refine. For readers who want to build and deploy neural network models with PyTorch, this recipe collection offers a direct path through the techniques that come up again and again.