Deep Learning and Generative AI Fundamentals

Artificial intelligence is evolving rapidly, making a strong grasp of core deep learning concepts essential for anyone entering the field. This course provides a structured, reading-based pathway through neural network foundations, transformer architectures, and modern generative models without unnecessary complexity. You will gain a solid intuitive and technical understanding of how deep learning systems process information, train on data, and generate novel content like text and images. What you'll learn: - Understand core neural network architectures, activation functions, loss calculations, and backpropagation mechanics - Learn the fundamentals of transformer models and attention mechanisms behind modern generative systems - Explore foundational generative models including diffusion approaches and autoregressive language models - Apply basic prompt engineering principles and understand retrieval-augmented generation (RAG) workflows - Work with vector embeddings and semantic search concepts used in modern AI pipelines - Practice evaluating model outputs and addressing common generative challenges like hallucination and bias The material begins with essential AI terminology, foundational math concepts, and model mechanics before guiding you through structured text explanations and practical code walkthroughs. This course is designed for beginners, developers, and tech enthusiasts who want a solid foundation in deep learning and generative AI without requiring advanced prior experience in machine learning. Start reading today to build your foundation in modern generative artificial intelligence.