Neural Network Training for Binary Image Classification

Computer vision starts with understanding how computers interpret visual patterns. If you want to move beyond pre-built libraries and truly understand how neural networks process and classify visual data, learning the fundamentals of feature extraction is essential. This text-only course guides you through the entire pipeline of binary image classification. You will transition from treating images as raw pixel grids to extracting meaningful edge features and using them to train a custom neural network. What you'll learn: - Understand the core concepts of digital images, pixel representations, and binary classification tasks. - Apply Sobel edge detection to extract structural features and boundaries from raw image data. - Prepare and format image datasets for training using modern Python conventions and type hints. - Build a foundational neural network architecture designed for binary classification. - Train your model systematically and evaluate its accuracy using standard performance metrics. - Practice debugging and optimizing your classification pipeline through structured code examples. You will begin with essential terminology and image processing fundamentals before diving into feature engineering and model training. Through detailed written explanations and clear code snippets, you will gain a practical understanding of how computer vision models make decisions. This course is designed for beginners eager to explore machine learning and computer vision. No prior deep learning experience is required, though a basic understanding of Python is recommended. Start your journey into computer vision and build your first image classifier today.