Week 1 From Biological Inspiration to Universal Approximation
Автор: Learn with Dr. Fakhreldeen
Загружено: 2026-01-05
Просмотров: 30
Описание: A comprehensive overview of the foundational concepts of neural computation, tracing the evolution from the biological neuron to early mathematical models like the McCulloch-Pitts model and the Perceptron algorithm, which first introduced the crucial element of mistake-driven learning. A key historical turning point is addressed through the XOR problem, which proved that single-layer networks could only solve linearly separable problems, thereby necessitating the development of Multilayer Perceptrons (MLPs). The expressive power of deep networks depends entirely on using non-linear activation functions—such as the modern default, ReLU—which prevents the network from collapsing into a single linear operation and grants the capacity for universal approximation. The text concludes by detailing the essential training metrics required for optimising these models, namely Mean Squared Error (MSE) for regression tasks and Cross-Entropy loss for classification problems.
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