Explore a comprehensive seminar on deep learning fundamentals and practical applications. Delve into topics including perceptrons, neural network architectures, gradient descent, backpropagation, and object recognition. Examine the brain-inspired design of deep learning models, focusing on vision and somatosensory systems. Investigate advanced concepts such as local receptive fields, weight sharing, kernels, and edge detection. Learn about three-dimensional networks, pooling techniques, and regularization methods. Gain insights from Richard Zemel, a distinguished researcher from the University of Toronto, as he presents at the Institute for Advanced Study's Computer Science/Discrete Mathematics Seminar II.