Foundations of Deep Learning for Computer Vision

How do computers see, understand, and interpret digital images? This text-based course guides you from the fundamental principles of image processing to building modern deep learning models for computer vision tasks. You will start by learning how digital images are represented and manipulated, then progress to building convolutional neural networks (CNNs) from scratch to detect edges, textures, and complex shapes. Finally, you will explore advanced architectures and modern concepts like transfer learning and Vision Transformers that power today's state-of-the-art vision applications. What you will learn: 1. Understand the core concepts of image processing and digital representation. 2. Build and train convolutional neural networks (CNNs) for image classification. 3. Apply transfer learning to adapt pre-trained models to custom datasets. 4. Explore modern vision architectures, including Vision Transformers (ViTs). 5. Implement key computer vision tasks like object detection and image segmentation. 6. Prepare and augment image data to improve model generalization. The course begins with foundational definitions and basic image processing concepts before moving step-by-step into deep learning architectures. Through clear explanations and structured code walk-throughs, you will gain a practical understanding of how to solve real-world vision problems. This course is designed for beginners in machine learning and computer vision. No prior experience with deep learning is required, though a basic understanding of Python is helpful. Start reading today to build your foundation in computer vision and deep learning.