A Survey of Deep Learning: From Activations to Transformers

The past decade has witnessed remarkable advancements in deep learning, owing to the emergence of various architectures, layers, objectives, and optimization techniques. These consist of a multitude of variations of attention, normalization, skip connections, transformer, and self-supervised learning methods, among others. Our goal is to furnish a comprehensive survey of significant recent contributions in these domains to individuals with a fundamental grasp of deep learning. Our aspiration is that an integrated and comprehensive approach of influential recent works will facilitate the formation of new connections between different areas of deep learning. In our discussion, we discuss multiple patterns that summarize the key strategies for many of the successful innovations over the last decade. We also include a discussion on recent commercially built, closed-source models such as OpenAI's GPT-4 and Google's PaLM 2.