Introduction to CNNs and Object Detection with YOLO

Deep learning has revolutionized how computers see the world, but understanding the underlying mechanics of computer vision can feel overwhelming. This text-based course demystifies the core components of Convolutional Neural Networks (CNNs) and shows you how they power modern object detection systems. Through clear written explanations, practical code walk-throughs, and step-by-step conceptual exercises, you will transition from a beginner to confidently understanding how neural networks locate and classify multiple objects in an image. What you'll learn: 1. Understand foundational CNN components including convolutions, activation functions, pooling, and padding. 2. Analyze how feature maps extract spatial hierarchies and patterns from images. 3. Explore the architecture of YOLO (You Only Look Once) for real-time object detection. 4. Apply modern data augmentation and preprocessing techniques to improve model performance. 5. Practice configuring loss functions, anchor boxes, and bounding box regression. 6. Evaluate object detection models using standard metrics like Intersection over Union (IoU) and Mean Average Precision (mAP). You will start by mastering foundational definitions and structural concepts of CNNs before advancing to the architecture of state-of-the-art object detection systems. The course concludes with practical guidance on configuring and tuning modern YOLO models for real-world scenarios. This course is designed for beginners in deep learning and computer vision. No advanced background in neural networks is required, though a basic familiarity with Python will help you get the most out of the written examples. Start reading today to build your foundation in modern computer vision.