Dive into the second part of a comprehensive three-part video series on VQGAN, focusing on Variational AutoEncoders (VAE) and Vector Quantized VAE (VQ-VAE). Explore the limitations of traditional autoencoders and how VAEs address these issues. Gain a deep understanding of VQ-VAE, including its introduction, key concepts, and the crucial role of codebooks in vector quantization. Learn how codebooks are created and their features, with a thorough summary of VQ-VAE principles. Access GitHub resources for hands-on practice and benefit from a detailed recap to solidify your knowledge of these advanced machine learning concepts.