Vision Transformers for Image Classification

While convolutional neural networks have dominated computer vision for years, attention-based Vision Transformers have emerged as a powerful alternative for image analysis. This text-only course guides you through the core concepts of Vision Transformers, helping you transition from traditional convolutional networks to modern transformer architectures for image classification. You will learn to: * Understand the foundational mechanics of self-attention and how it applies to 2D image patches * Explore the structural differences between classic convolutional networks and Vision Transformers * Implement a basic Vision Transformer architecture using clear, step-by-step code walkthroughs * Analyze modern hybrid models and efficient transformer variants designed for resource-constrained environments * Practice evaluating and fine-tuning pre-trained transformer models for custom classification tasks You will start with key terminology and foundational attention mechanisms before progressing to patch projection, positional embeddings, and practical implementation patterns. Designed for beginners in deep learning and computer vision, this course requires only basic Python knowledge and no prior experience with transformer models. Start reading today to master the next generation of computer vision architectures.