Activation functions in artificial neural networks define the output of a node given an input or set of inputs. Standard integrated circuits can be seen as digital networks of these activation functions, which are either "ON" or "OFF". Nonlinear activation functions allow networks to solve complex problems with fewer nodes.

Stanford University

Spring 2022

This is a deep-dive into the details of deep learning architectures for visual recognition tasks. The course provides students with the ability to implement, train their own neural networks and understand state-of-the-art computer vision research. It requires Python proficiency and familiarity with calculus, linear algebra, probability, and statistics.

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Spring 2022

Brown University's Deep Learning course acquaints students with the transformative capabilities of deep neural networks in computer vision, NLP, and reinforcement learning. Using the TensorFlow framework, topics like CNNs, RNNs, deepfakes, and reinforcement learning are addressed, with an emphasis on ethical applications and potential societal impacts.

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