Fundamentals of Neural Networks for Image Classification

Image classification is at the heart of modern computer vision, powering everything from medical diagnostics to autonomous vehicles. To build these intelligent systems, you must understand how artificial neural networks process visual data and learn from experience. This text-based course guides you step-by-step through the core mathematical and programming concepts required to build your first image classifiers from scratch. You will transition from understanding basic biological inspiration to writing clean, modular PyTorch code for training deep learning models. Along the way, you will explore modern best practices, including proper data pipeline setup and model evaluation techniques. What you'll learn: - Understand the core architecture of neural networks, including layers, weights, biases, and activation functions - Implement supervised learning workflows for image classification tasks using PyTorch - Prepare and preprocess image datasets using modern data loading and augmentation pipelines - Train neural networks using loss functions, backpropagation, and optimization algorithms - Evaluate model performance using key metrics like accuracy, precision, recall, and confusion matrices - Apply modern training techniques such as learning rate scheduling and early stopping to prevent overfitting This course begins with foundational concepts, defining artificial neurons, feedforward propagation, and the role of tensors. You will then progress to practical implementation, writing clean PyTorch code to load image data, build network architectures, and run training loops. This course is designed for beginners who want a solid, code-first introduction to deep learning for computer vision. No prior experience with neural networks or machine learning is required, though basic Python familiarity is helpful. Start reading today to build a strong foundation in neural networks and image classification.