Optimization in Machine Learning: Gradient Descent
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Covers the concept of gradient descent in scalar cases, focusing on finding the minimum of a function by iteratively moving in the direction of the negative gradient.
Covers the history and inspiration behind artificial neural networks, the structure of neurons, learning through synaptic connections, and the mathematical description of artificial neurons.
Covers the BackProp algorithm, including initialization, signal propagation, error computation, weight updating, and complexity comparison with numerical differentiation.
