A Survey of Deep Learning: From Activations to Transformers

Deep learning has made tremendous progress in the last decade. A key success factor is the large amount of architectures, layers, objectives, and optimization techniques. They include a myriad of variants related to attention, normalization, skip connections, transformers and self-supervised learning schemes -- to name a few. We provide a comprehensive overview of the most important, recent works in these areas to those who already have a basic understanding of deep learning. We hope that a holistic and unified treatment …

architectures attention cs.ai cs.lg deep learning key normalization optimization progress self-supervised learning success supervised learning survey transformers variants