Modern Computer Vision: Foundations and Deep Learning

Computer vision is at the heart of modern artificial intelligence, powering everything from autonomous systems to medical diagnostics. Understanding how computers interpret visual data is an essential skill for today's software developers and AI enthusiasts. This text-based course provides a structured pathway to master the field, guiding you from basic image representation to state-of-the-art deep learning techniques. What you'll learn: - Understand the core concepts of digital image processing, filtering, and feature extraction. - Build and configure convolutional neural networks (CNNs) from scratch using PyTorch. - Apply transfer learning to leverage pre-trained modern models for custom classification tasks. - Implement object detection and image segmentation pipelines through clear, step-by-step code analysis. - Explore modern vision transformers (ViTs) and their applications in cutting-edge visual recognition. - Practice designing robust computer vision workflows using clear written explanations and structured exercises. The course begins with foundational concepts of pixel manipulation and classical filters, gradually advancing to deep neural networks and modern transformer architectures. This course is ideal for beginners, programmers, and aspiring data scientists who want to build a solid foundation in visual AI without needing prior computer vision experience. Begin your journey into the world of visual intelligence today.