Modern Deep Learning

Modern deep learning focuses on developing and improving algorithms and architectures for training large-scale neural networks, aiming to enhance efficiency, accuracy, and generalizability. Current research emphasizes efficient training methods under time constraints, scalable optimization techniques for massive models (including transformers and convolutional networks), and improved uncertainty quantification through Bayesian approaches and novel regularization methods like weight decay. These advancements are crucial for deploying deep learning in resource-limited environments (e.g., edge devices, federated learning) and for addressing challenges in areas such as space weather prediction and personalized medicine.