Composing Music with Generative AI: RNNs, GANs, and Transformers

Artificial intelligence is transforming how we create art, and music composition is at the forefront of this revolution. Understanding how algorithms generate melodies, harmonies, and full audio tracks opens up new creative and technical possibilities. This course guides you through the core concepts of generative music models, teaching you how to represent music for machine learning and implement key architectures. You will transition from reading about foundational music theory representations to understanding how modern AI models compose original pieces. What you'll learn: - Understand the differences between symbolic music representations like MIDI and direct audio generation. - Explore how Recurrent Neural Networks (RNNs) process and generate sequential musical notes. - Analyze the role of Generative Adversarial Networks (GANs) in creating complex multi-track compositions. - Learn the basics of modern transformer-based models and attention mechanisms applied to music. - Practice writing code snippets to preprocess musical data and configure generative architectures. - Discover how to evaluate and refine AI-generated music for artistic quality. We begin with the absolute basics of musical data representation, ensuring you have a solid conceptual foundation before moving into neural network architectures. You will then progress through step-by-step written explanations of RNNs, GANs, and modern transformers, accompanied by clear code examples. This text-based course is designed for beginners interested in the intersection of music and artificial intelligence; no prior background in machine learning or music theory is required. Start reading today to unlock the potential of AI-driven musical creativity.