Delves into the spectral bias of polynomial neural networks, analyzing the impact on learning different frequencies and discussing experimental results.
Introduces the fundamentals of deep learning, covering neural networks, CNNs, special layers, weight initialization, data preprocessing, and regularization.
Explores challenges and opportunities in vision-based robotic perception, covering topics like SLAM, place recognition, event cameras, and collaborative visual intelligence.
Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.
