Deep Learning: A Taxonomy and Research Roadmap

This technical report presents a structured taxonomy and research roadmap for deep learning, distinguishing among neural architectures, computational mechanisms, learning paradigms, task-specific architectures, and complete AI systems. The report surveys foundational neural networks, convolutional neural networks, recurrent and sequence-to-sequence architectures, attention mechanisms, Transformer architectures, vision Transformers, generative models, graph deep learning, state-space models, neural differential equations, and deep reinforcement learning. It further examines self-supervised and multimodal learning, mixture-of-experts architectures, meta-learning, continual learning, federated learning, computer vision systems, speech and audio models, time-series modeling, recommendation architectures, metric learning, domain adaptation, knowledge distillation, causal deep learning, neuro-symbolic AI, and emerging neural architectures. The report also covers training and optimization methods, normalization and regularization, large language model efficiency, parameter-efficient fine-tuning, model compression, efficient inference, LLM training and alignment, retrieval-augmented generation, agentic AI, and foundation-model systems. Each major method family is organized around milestone tables, mathematical formulations, and descriptions of its historical development. The report concludes with an overview of the evolution of deep learning, research-oriented mastery levels, and a structured research pathway for natural language processing and large language models. Document type: Technical report / research roadmap Author: Asifur Rahaman