AI Music Generation: Building GANs and RNNs for Musical Sequences

Discover how artificial intelligence can compose original melodies by combining sequential deep learning with adversarial networks. This text-based course guides you through the fundamental intersections of music representation and modern machine learning. You will transition from understanding basic musical data formats to building and training generative models capable of composing new monophonic sequences. You will learn to represent musical notes as digital tokens and train neural networks to find patterns in rhythm and pitch. What you will learn: Understand how to represent musical data digitally using MIDI and sequence tokenization; Build Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models for sequential patterns; Configure Bidirectional LSTMs to capture both past and future musical context; Design Generative Adversarial Networks (GANs) adapted for discrete musical sequences; Apply adversarial training techniques to refine the quality of generated melodies; Evaluate AI-generated music and export model outputs into playable formats. The course begins with foundational concepts of musical representation in code, establishing a solid ground before moving into model architectures. You will progress from basic recurrent layers to advanced adversarial training loops, analyzing written code implementations along the way. This course is designed for beginners in AI music generation and developers with basic Python knowledge who want to explore generative art; no prior background in music theory or advanced deep learning is required. Start reading today to build your first generative music model from the ground up.