Computer Vision Foundations with PyTorch and TensorFlow

Computer vision is transforming industries from healthcare to autonomous driving, but getting started requires a solid grasp of both core theory and practical frameworks. This text-based course guides you step-by-step from fundamental pixel manipulations to training state-of-the-art deep learning models. You will transition from a beginner to a confident practitioner capable of designing, training, and evaluating neural networks. By reading through clear explanations and structured code snippets, you will understand exactly how machines interpret visual data and how to apply these concepts to real-world scenarios. What you'll learn: - Understand core image representation, color spaces, and preprocessing techniques using OpenCV. - Build and train Convolutional Neural Networks (CNNs) from scratch in both PyTorch and TensorFlow. - Apply transfer learning using pre-trained architectures like ResNet and modern Vision Transformers (ViTs). - Implement object detection models including YOLO and Faster R-CNN for localized predictions. - Configure semantic segmentation pipelines using U-Net architectures for pixel-level classification. - Optimize model training with advanced data augmentation and modern dataset pipeline practices. The course begins with foundational image processing and neural network basics before progressing to advanced deep learning architectures. You will explore structured code implementations for image classification, object detection, and segmentation tasks, learning how to debug and refine your models. This course is designed for beginners, aspiring data scientists, and software developers looking to enter the field of artificial intelligence. No prior deep learning experience is required, though a basic understanding of Python programming is recommended. Start reading today to unlock the potential of computer vision and build your first intelligent visual applications.