Affective Computing: Theories, Applications, and Predictive Models

Related lectures (32)

Explores optimization and approximation in deep neural networks, including optimal control and numerical experiments.

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

Delves into students' emotional experiences in the first year at university, focusing on achievement emotions and their implications for practice.

Explores the use of colors in film to convey emotions and set the mood.

Explores the impact of emotions on ethics education and ethical decision-making processes.

Emphasizes selecting images to evoke positive emotions and clarify training content in digital education.

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Explores the Brookfield reflection model and strategies for managing emotions triggered by feedback.

Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

Explores the optimality of splines for imaging and deep neural networks, demonstrating sparsity and global optimality with spline activations.

Explores emotions, coping with challenges, software malfunctions, difficult exercises, and locus of control in stress management.

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