Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI, Second Edition

A Practical Roadmap to Computer Vision with PyTorch

Modern Computer Vision with PyTorch, Second Edition is a hands-on guide to building computer vision systems with PyTorch. Written by V Kishore Ayyadevara and Yeshwanth Reddy and published by Packt in 2024, this edition expands the original 2020 roadmap into a deeper, more current treatment of deep learning for images—including the generative AI developments that have reshaped the field.

From Neural Network Foundations to Real-World Models 💻

The book opens with the groundwork: artificial neural network fundamentals, feedforward propagation, backpropagation, loss functions, learning rates, and the PyTorch tensor model. From there, it moves into building deeper networks, preparing image data, scaling datasets, tuning batch sizes, optimizers, batch normalization, dropout, and regularization. These are not abstract lessons; the material is organised around implementation, so readers can follow the reasoning and then see it expressed in code.

Object Classification, Detection, and Beyond 👁️

Later sections turn to convolutional neural networks and transfer learning. You’ll explore convolution, filters, strides, padding, pooling, and the ways CNNs learn visual features. The book then looks at transfer learning with architectures such as VGG16, showing how pre-trained models can be adapted to new image classification tasks. The subtitle also signals coverage of advanced applications and Generative AI, connecting the core vision toolkit to the newer wave of generative models.

Who This Book Is For 🧠

This is a technical, project-oriented book for readers who want to build computer vision models rather than just read about them. It suits data scientists, machine learning engineers, software developers, and students who already have some Python and deep learning familiarity and want a structured path into PyTorch-based vision work. Beginners may find the early chapters useful for filling gaps, but the pace and scope assume a willingness to engage with code and mathematical concepts.

Why the Second Edition Matters

Computer vision has moved quickly since the first edition. This second edition updates the roadmap with newer architectures, practical tuning advice, and generative AI topics, while keeping the focus on implementation. If you want a current, substantial reference for moving from neural network basics to modern vision applications, this edition is built for that journey.

• PyTorch tensors, autograd, and model-building workflows
• Feedforward and backpropagation fundamentals
• CNNs, convolution, pooling, and feature visualisation
• Transfer learning with VGG16 and related architectures
• Batch normalization, dropout, L1/L2 regularization
• Image classification, object detection, and generative AI applications