Welcome to Volume 1, Issue 1 of Advances in Deep Learning Techniques, where we embark on a journey into the forefront of deep learning research. In this inaugural edition, we present two pioneering papers that delve into critical aspects of deep learning innovation. "Residual Networks - Architectural Innovations and Beyond" explores the architectural innovations and applications of Residual Networks (ResNets), shedding light on techniques for improving training efficiency and performance in deep learning tasks. Concurrently, "Generative Adversarial Networks - Recent Developments" investigates the latest advancements in Generative Adversarial Networks (GANs), offering insights into their capabilities for generating realistic images and other data types. Join us as we explore the cutting-edge of deep learning, paving the way for transformative breakthroughs in artificial intelligence and machine learning.