Delves into the challenges and benefits of deep learning, highlighting the transition to convolutional neural networks and the impact of network width on the loss landscape.
Explores the evolution of vision theories and visual feedback systems, including bottom-up and top-down approaches, and iterative error feedback for human pose estimation.
Explores the influence of computational linguistics on deep learning architectures, covering grammar formalisms, connectionism, variable binding, and future directions.
Explores Parkinson's disease, covering its history, challenges in diagnosis, available treatments, neuropathological changes, and impact on motor control.