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Explores optimal errors in high-dimensional models, comparing algorithms and shedding light on the interplay between model architecture and performance.
Covers the history and inspiration behind artificial neural networks, the structure of neurons, learning through synaptic connections, and the mathematical description of artificial neurons.
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
