Foundations of Deep Learning - Part 1
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Foundations of Deep Learning - Part 1
Foundations introduce terminology, regression, and directed computational graphs starting from the perceptron; layered nonlinear networks generalize this to deep models. Training centers on a loss function, gradient descent, and backpropagation.
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Requirements
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General Overview
Description
Foundations of Deep Learning — Introduction establishes core terminology, frames regression as a baseline task, and represents computations as directed graphs, beginning with the famous perceptron that motivates multilayer extensions. Perceptrons and Neural Networks develop layered architectures with nonlinearity, showing how depth enables complex function approximation beyond linear decision boundaries. Neural Network Training formalizes objectives with a loss function, optimizes parameters via gradient descent, and computes exact gradients through backpropagation over the computational graph.
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Meet the instructors !
Stefan received a PhD from the University of Hamburg in 2007, where he also completed his habilitation on decision analysis and support using ensemble forecasting models in 2012. He then joined the Humboldt-University of Berlin in 2014, where he heads the Chair of Information Systems at the School of Business and Economics. He serves as an associate editor for the International Journal of Business Analytics, Digital Finance, and the International Journal of Forecasting, and as department editor of Business and Information System Engineering (BISE). Stefan has secured substantial amounts of research funding and published several papers in leading international journals and conferences. His research concerns the support of managerial decision-making using quantitative empirical methods. He specializes in applications of (deep) machine learning techniques in the broad scope of marketing and risk analytics. Stefan actively participates in knowledge transfer and consulting projects with industry partners; from start-up companies to global players and not-for-profit organizations.