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Covers the history and fundamental concepts of neural networks, including the mathematical model of a neuron, gradient descent, and the multilayer perceptron.
Covers the history and inspiration behind artificial neural networks, the structure of neurons, learning through synaptic connections, and the mathematical description of artificial neurons.
Explores the learning dynamics of deep neural networks using linear networks for analysis, covering two-layer and multi-layer networks, self-supervised learning, and benefits of decoupled initialization.
