Welcome to Volume 3, Issue 2 of Advances in Deep Learning Techniques, where we delve into the latest advancements and applications shaping the landscape of deep learning research. In this edition, we present two seminal papers that illuminate essential aspects of deep learning innovation. "Transformer Networks - Architectures and Applications" navigates the complexities of transformer network architectures and their diverse applications, particularly in natural language processing and beyond. Concurrently, "Variational Autoencoders - Theory and Applications" explores variational autoencoder models and their versatile applications in generative modeling, representation learning, and beyond. Join us as we unravel the intricacies of deep learning, fostering innovation and excellence in artificial intelligence and machine learning.