Computer Vision Foundations: Building CNNs with PyTorch and fastai

Computer vision is transforming how we analyze visual data, but understanding the underlying mechanics of Convolutional Neural Networks (CNNs) is essential to building models that actually work. This text-based course guides you through the core concepts of deep learning for computer vision, taking you from foundational mathematical operations to training robust neural networks. You will learn to build and optimize models using PyTorch and the fastai library, focusing on practical, code-first implementation. By reading this course, you will transition from a beginner to a practitioner capable of designing and training custom image classifiers. You will understand not just how to run the code, but how convolutions, pooling layers, and activation functions manipulate data to extract meaningful features from images. What you'll learn: - Understand the core mathematical concepts of convolutions, kernels, and padding - Build image classification models using PyTorch and the fastai framework - Apply transfer learning techniques to adapt pre-trained models to custom datasets - Configure model hyperparameters, learning rates, and optimization algorithms - Practice debugging and improving model accuracy using validation metrics - Analyze modern CNN architectures and understand how they process spatial data The course begins with foundational definitions of neural networks, tensor operations, and image representation in code. From there, you will step through the mechanics of a single convolutional layer before scaling up to complete architectures, training loops, and performance tuning. This course is designed for programmers, data analysts, and beginners who want to learn deep learning for computer vision. No prior machine learning experience is required, though basic familiarity with Python is recommended. Start reading today to build your first deep learning models for computer vision.