Foundations of Deep Learning and Reinforcement Learning

Artificial intelligence is transforming industries, but the mechanics behind neural networks and autonomous decision-making often seem like a black box. This course breaks down these complex systems into readable, manageable concepts designed for those new to the field. You will transition from knowing basic machine learning to understanding how deep architectures process complex data and how reinforcement learning enables agents to learn and improve through environmental interaction. What you'll learn: - Understand the mathematical foundations of neural networks and the backpropagation process - Explore modern architectures including convolutional layers and the basics of transformer models - Apply reinforcement learning principles to model agent-environment interactions - Learn the mechanics of Markov Decision Processes and the significance of reward functions - Master the core concepts of Q-learning and policy gradients for autonomous decision-making - Practice identifying the right deep learning structures for different data challenges The course begins with essential terminology and the foundational history of neural networks before moving into specific deep learning architectures and the logic of reinforcement learning. You will progress through written explanations and code-based logic exercises that reinforce every concept. This course is designed for beginners who want to move beyond basic data science; no prior experience with deep learning or reinforcement learning is required. Start your journey into the core technologies driving today's AI innovations.