Deep Learning and AI Fundamentals: Neural Networks to LLMs

Artificial intelligence and machine learning are transforming how we build software, but mastering the core concepts of deep learning can feel overwhelming. This course simplifies these complex topics, breaking down neural networks and modern AI techniques into clear, readable explanations. You will transition from a curious beginner to someone who understands how neural networks learn, how natural language processing operates, and how large language models function in cloud environments like AWS. What you'll learn: 1. Understand the core architecture of neural networks and how they process data. 2. Differentiate between classical machine learning, deep learning, and modern generative AI. 3. Explore the mechanics of Natural Language Processing (NLP) and Large Language Models (LLMs). 4. Learn the fundamentals of modern AI architectures, including vector databases and prompt engineering basics. 5. Discover how deep learning workloads are structured and deployed in cloud ecosystems. 6. Practice analyzing AI model behaviors and training workflows through written conceptual exercises. The course begins with essential terminology and the foundational mathematical concepts behind AI learning methods. From there, you will read through structured modules covering deep learning architectures, modern language models, and cloud-based AI deployment strategies. This text-based course is designed for absolute beginners, aspiring cloud practitioners, and anyone looking to build a solid conceptual foundation in AI without needing a background in advanced mathematics or programming. Start reading today to unlock the core principles driving modern artificial intelligence.