This article reviews the evolution of deep learning, its key concepts, model architectures, training strategies, and applications—especially in speech, vision, and natural language processing—highlighting seminal research, comparative analyses, and current challenges for future AI development.
This article reviews the evolution of pre‑trained models in natural language processing, from early word embeddings to Transformer‑based architectures like BERT and its variants, outlines their wide‑range applications such as QA, translation, and dialogue, and discusses remaining challenges and future research directions.
MedicalNet, Tencent’s open-source framework, aggregates diverse small 3D medical imaging datasets into a large pre-training corpus, applies dataset filtering and joint spatial-pixel normalization, and provides encoder-decoder models that accelerate convergence and boost accuracy for AI-driven diagnosis in data-scarce medical imaging scenarios.
