Neural Networks and AI: Concepts from Neuron to Transformer

Artificial intelligence seems like magic, but at its core, it relies on foundational mathematical and architectural principles. This course strips away the complexity to reveal how modern machine intelligence truly works. By the end of this course, you will have a solid conceptual understanding of neural network components, training processes, and the structure of cutting-edge models like Transformers, giving you the clarity needed to apply these concepts effectively. What you'll learn: * Understand the fundamental structure of a single neuron and how it processes information. * Master the core concepts of training, including loss functions, optimization, and backpropagation. * Analyze the architecture of deep learning models, including convolutional and recurrent networks. * Apply the principles of the attention mechanism and the state-of-the-art Transformer architecture. * Practice interpreting model outputs and basic prompt engineering for generative AI systems. * Configure virtual environments and manage dependencies for machine learning projects. We begin by defining key terminology and exploring the mathematics behind simple perceptrons. The material then progresses through various network types and culminates in an analysis of advanced architectures used in today's most powerful AI applications. This course is designed for absolute beginners interested in understanding the inner workings of AI and neural networks. No prior programming or advanced mathematical knowledge is required. Start your journey into the architecture of artificial intelligence today.