Modern Computer Vision with PyTorch

Computer Vision with PyTorch, from First Principles to Real Projects

Computer vision has moved from research labs into everyday tools: photo tagging, medical imaging, autonomous systems, and more. Modern Computer Vision with PyTorch is a hands-on guide by V Kishore Ayyadevara and Yeshwanth Reddy that walks through the ideas and code behind modern image applications using PyTorch.

The book begins with the building blocks—artificial neural networks, feedforward propagation, backpropagation, loss functions, and gradient descent—before introducing PyTorch tensors, autograd, and the mechanics of training a neural network in code. From there, it builds toward deeper models and convolutional architectures.

From Neural Networks to Convolutional Models 🧠

Early chapters establish a clear foundation: how a neural network learns, why learning rate and batch size matter, and how scaling, batch normalization, dropout, and regularization affect training. Readers then move into convolutional neural networks, covering convolution, filters, strides, padding, pooling, and data augmentation.

The progression is practical. Concepts are paired with PyTorch implementations, so the material connects theory to working code rather than leaving the math on its own.

Transfer Learning and Practical Image Classification 📷

Later sections address transfer learning for image classification, including VGG16 and ResNet architectures. The table of contents also covers facial key point detection, multi-task learning for age estimation and gender classification, class activation maps, imbalanced data, and the practical details that affect model performance.

The subtitle promises over 50 real-world image applications, and the book keeps that applied focus throughout: building, training, evaluating, and improving computer vision models with PyTorch.

Who This Book Is For

This is a technical resource for data scientists, machine learning engineers, developers, and students who want to build computer vision systems with PyTorch. It is especially useful for readers who prefer learning through implementation and want a structured path from neural network fundamentals to convolutional and transfer-learning workflows.

Why It Works as a Learning Path

Rather than treating computer vision as a collection of isolated techniques, the book shows how data preparation, model architecture, training choices, and evaluation fit together. The PyTorch-first approach means examples are written in a framework widely used in research and production.

For anyone building a foundation in deep learning for images, Modern Computer Vision with PyTorch offers a detailed, code-oriented route through the subject.

Start Building with PyTorch 💻

Add this Packt Publishing guide to your digital library and work through the concepts, code, and image applications at your own pace.