Foundations of Deep Learning and Reinforcement Learning

Deep learning and reinforcement learning power the most advanced artificial intelligence systems today, from autonomous agents to generative models. Understanding how these technologies work from first principles is essential for anyone entering the modern AI landscape. This text-based course guides you step-by-step through the core concepts, mathematical foundations, and practical implementations of neural networks and decision-making agents. You will transition from learning basic terminology to reading and writing clean deep learning code. First, you will learn the foundational mathematics and core terminology of artificial neural networks. Then, you will build and configure deep learning models using modern framework conventions. You will also understand the mechanics of convolutional and recurrent architectures for complex data. Additionally, you will explore modern transformer models that drive state-of-the-art natural language processing. Next, you will apply reinforcement learning principles to train agents in simulated environments. Finally, you will practice implementing training loops, optimization techniques, and evaluation metrics. The course begins with essential definitions and mathematical prerequisites before moving systematically into model architecture design, training strategies, and modern reinforcement learning paradigms. You will study clear code examples and conceptual breakdowns to reinforce your understanding. This course is designed for beginners who want to establish a solid theoretical and practical foundation in AI, with no prior deep learning experience required. Start your journey into the world of advanced artificial intelligence today.